Data mining Books

Book SynopsisMachine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel.

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Book SynopsisA new approach to unsupervised learning Evolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challengefor the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers. Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, Unsupervised Learning: A Dynamic Approach presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia datafrom its role in generic image processing techniques, such as thTable of ContentsAcknowledgments xi 1 Introduction 1 1.1 Part I: The Self-Organizing Method 1 1.2 Part II: Dynamic Self-Organization for Image Filtering and Multimedia Retrieval 2 1.3 Part III: Dynamic Self-Organization for Image Segmentation and Visualization 5 1.4 Future Directions 7 2 Unsupervised Learning 9 2.1 Introduction 9 2.2 Unsupervised Clustering 9 2.3 Distance Metrics for Unsupervised Clustering 11 2.4 Unsupervised Learning Approaches 13 2.4.1 Partitioning and Cluster Membership 13 2.4.2 Iterative Mean-Squared Error Approaches 15 2.4.3 Mixture Decomposition Approaches 17 2.4.4 Agglomerative Hierarchical Approaches 18 2.4.5 Graph-Theoretic Approaches 20 2.4.6 Evolutionary Approaches 20 2.4.7 Neural Network Approaches 21 2.5 Assessing Cluster Quality and Validity 21 2.5.1 Cost Function–Based Cluster Validity Indices 22 2.5.2 Density-Based Cluster Validity Indices 23 2.5.3 Geometric-Based Cluster Validity Indices 24 3 Self-Organization 27 3.1 Introduction 27 3.2 Principles of Self-Organization 27 3.2.1 Synaptic Self-Amplification and Competition 27 3.2.2 Cooperation 28 3.2.3 Knowledge Through Redundancy 29 3.3 Fundamental Architectures 29 3.3.1 Adaptive Resonance Theory 29 3.3.2 Self-Organizing Map 37 3.4 Other Fixed Architectures for Self-Organization 43 3.4.1 Neural Gas 44 3.4.2 Hierarchical Feature Map 45 3.5 Emerging Architectures for Self-Organization 46 3.5.1 Dynamic Hierarchical Architectures 47 3.5.2 Nonstationary Architectures 48 3.5.3 Hybrid Architectures 50 3.6 Conclusion 50 4 Self-Organizing Tree Map 53 4.1 Introduction 53 4.2 Architecture 54 4.3 Competitive Learning 55 4.4 Algorithm 57 4.5 Evolution 61 4.5.1 Dynamic Topology 61 4.5.2 Classification Capability 64 4.6 Practical Considerations, Extensions, and Refinements 68 4.6.1 The Hierarchical Control Function 68 4.6.2 Learning, Timing, and Convergence 71 4.6.3 Feature Normalization 73 4.6.4 Stop Criteria 73 4.7 Conclusions 74 5 Self-Organization in Impulse Noise Removal 75 5.1 Introduction 75 5.2 Review of Traditional Median-Type Filters 76 5.3 The Noise-Exclusive Adaptive Filtering 82 5.3.1 Feature Selection and Impulse Detection 82 5.3.2 Noise Removal Filters 84 5.4 Experimental Results 86 5.5 Detection-Guided Restoration and Real-Time Processing 99 5.5.1 Introduction 99 5.5.2 Iterative Filtering 101 5.5.3 Recursive Filtering 104 5.5.4 Real-Time Processing of Impulse Corrupted TV Pictures 105 5.5.5 Analysis of the Processing Time 109 5.6 Conclusions 115 6 Self-Organization in Image Retrieval 119 6.1 Retrieval of Visual Information 120 6.2 Visual Feature Descriptor 122 6.2.1 Color Histogram and Color Moment Descriptors 122 6.2.2 Wavelet Moment and Gabor Texture Descriptors 123 6.2.3 Fourier and Moment-based Shape Descriptors 125 6.2.4 Feature Normalization and Selection 127 6.3 User-Assisted Retrieval 130 6.3.1 Radial Basis Function Method 132 6.4 Self-Organization for Pseudo Relevance Feedback 136 6.5 Directed Self-Organization 140 6.5.1 Algorithm 142 6.6 Optimizing Self-Organization for Retrieval 146 6.6.1 Genetic Principles 147 6.6.2 System Architecture 149 6.6.3 Genetic Algorithm for Feature Weight Detection 150 6.7 Retrieval Performance 153 6.7.1 Directed Self-Organization 153 6.7.2 Genetic Algorithm Weight Detection 155 6.8 Summary 157 7 The Self-Organizing Hierarchical Variance Map 159 7.1 An Intuitive Basis 160 7.2 Model Formulation and Breakdown 162 7.2.1 Topology Extraction via Competitive Hebbian Learning 163 7.2.2 Local Variance via Hebbian Maximal Eigenfilters 165 7.2.3 Global and Local Variance Interplay for Map Growth and Termination 170 7.3 Algorithm 173 7.3.1 Initialization, Continuation, and Presentation 173 7.3.2 Updating Network Parameters 175 7.3.3 Vigilance Evaluation and Map Growth 175 7.3.4 Topology Adaptation 176 7.3.5 Node Adaptation 177 7.3.6 Optional Tuning Stage 177 7.4 Simulations and Evaluation 177 7.4.1 Observations of Evolution and Partitioning 178 7.4.2 Visual Comparisons with Popular Mean-Squared Error Architectures 181 7.4.3 Visual Comparison Against Growing Neural Gas 183 7.4.4 Comparing Hierarchical with Tree-Based Methods 183 7.5 Tests on Self-Determination and the Optional Tuning Stage 187 7.6 Cluster Validity Analysis on Synthetic and UCI Data 187 7.6.1 Performance vs. Popular Clustering Methods 190 7.6.2 IRIS Dataset 192 7.6.3 WINE Dataset 195 7.7 Summary 195 8 Microbiological Image Analysis Using Self-Organization 197 8.1 Image Analysis in the Biosciences 197 8.1.1 Segmentation: The Common Denominator 198 8.1.2 Semi-supervised versus Unsupervised Analysis 199 8.1.3 Confocal Microscopy and Its Modalities 200 8.2 Image Analysis Tasks Considered 202 8.2.1 Visualising Chromosomes During Mitosis 202 8.2.2 Segmenting Heterogeneous Biofilms 204 8.3 Microbiological Image Segmentation 205 8.3.1 Effects of Feature Space Definition 207 8.3.2 Fixed Weighting of Feature Space 209 8.3.3 Dynamic Feature Fusion During Learning 213 8.4 Image Segmentation Using Hierarchical Self-Organization 215 8.4.1 Gray-Level Segmentation of Chromosomes 215 8.4.2 Automated Multilevel Thresholding of Biofilm 220 8.4.3 Multidimensional Feature Segmentation 221 8.5 Harvesting Topologies to Facilitate Visualization 226 8.5.1 Topology Aware Opacity and Gray-Level Assignment 227 8.5.2 Visualization of Chromosomes During Mitosis 228 8.6 Summary 233 9 Closing Remarks and Future Directions 237 9.1 Summary of Main Findings 237 9.1.1 Dynamic Self-Organization: Effective Models for Efficient Feature Space Parsing 237 9.1.2 Improved Stability, Integrity, and Efficiency 238 9.1.3 Adaptive Topologies Promote Consistency and Uncover Relationships 239 9.1.4 Online Selection of Class Number 239 9.1.5 Topologies Represent a Useful Backbone for Visualization or Analysis 240 9.2 Future Directions 240 9.2.1 Dynamic Navigation for Information Repositories 241 9.2.2 Interactive Knowledge-Assisted Visualization 243 9.2.3 Temporal Data Analysis Using Trajectories 245 Appendix A 249 A.1 Global and Local Consistency Error 249 References 251 Index 269

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Book SynopsisPresents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world, mission-critical applications.Trade Review' … buy the book. This book is definitely worth having in your book shelf as a handy reference.' IAPR NewsletterTable of Contents1. Introduction to text mining; 2. Core text mining operations; 3. Text mining preprocessing techniques; 4. Categorization; 5. Clustering; 6. Information extraction; 7. Probabilistic models for Information extraction; 8. Preprocessing applications using probabilistic and hybrid approaches; 9. Presentation-layer considerations for browsing and query refinement; 10. Visualization approaches; 11. Link analysis; 12. Text mining applications; Appendix; Bibliography.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis book examines cyber threat intelligence obtained from the center of the malicious hacking underworld - the dark web. It studies these communities both qualitatively and quantitatively, leveraging techniques from data mining, machine learning and AI, and offering insights to both cybersecurity practitioners and researchers.Trade Review'Darkweb Cyber Threat Intelligence Mining represents a tipping point in cyber security. It is a must-read for anyone involved in the modern cyber struggle.' George Cybenko, Dartmouth College, New Hampshire, from the Foreword'The book is well written and well structured. The authors provide interesting facts on the darknet economy, its community, and its underling rules, such as trust-based platforms and the related problems of its participants.' Steffen Wendzel, Computing ReviewsTable of Contents1. Introduction; 2. Moving to proactive cyber threat intelligence; 3. Understanding darkweb malicious hacker forums; 4. Automatic mining of cyber intelligence from the dark web; 5. Analyzing products and vendors in malicious hacking markets; 6. Using game theory for threat intelligence; 7. Application – protecting industrial control systems; 8. Conclusion – the future of darkweb cyber threat intelligence.

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Book SynopsisThis comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL, MongoDB, Neo4j Cypher, and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.Trade Review'Although there have been a series of classical textbooks on database systems, the new dramatic advances call for an updated text covering the latest significant topics, such as big data analytics, No-SQL and much more. Fortunately, this is exactly what this book has to offer. It is highly desirable for training the next generation of data management professionals.' Jian Pei, Simon Fraser University, Canada'I haven't seen an as up-to-date and comprehensive textbook for Database Management as this one in many years. Principles of Database Management combines a number of classical and recent topics concerning Data Modeling, Relational Databases, Object-Oriented Databases, XML, Distributed Data Management, NoSQL and Big Data in an unprecedented manner. The authors did a great job in stitching these topics into one coherent and compelling story that will serve as an ideal basis for teaching both introductory and advanced courses.' Martin Theobald, University of Luxembourg'This is a very timely book with outstanding coverage of database topics and excellent treatment of database details. It not only gives very solid discussions of traditional topics like data modeling and relational databases but also contains refreshing contents on frontier topics such as XML databases, NoSQL databases, big data, and analytics. For those reasons, this will be a good book for database professionals who will keep using it for all stages of database studies and works.' J. Leon Zhao, City University of Hong Kong'This accessible, authoritative book introduces the reader the most important fundamental concepts of data management, while providing a practical view of recent advances. Both are essential for data professionals today.' Foster Provost, New York University, Stern School of Business'This guide to big and small data management addresses both fundamental principles and practical deployment. It reviews a range of databases and their relevance for analytics. The book is useful to practitioners because it contains many case studies, links to open-source software, and a very useful abstraction of analytics that will help them better choose solutions. It is important to academics because it promotes database principles which are key to successful and sustainable data science.' Sihem Amer-Yahia, Laboratoire d'Informatique de Grenoble and Editor-in-Chief the International Journal on Very Large DataBases'This book covers everything you will need to teach in a database implementation and design class. With some chapters covering big data, analytic models/methods, and No-SQL, it can keep our students up-to-date with these new technologies in data management related topics.' Han-fen Hu, University of Nevada, Las Vegas'As we are entering a new technological era of intelligent machines powered by data-driven algorithms, understanding fundamental concepts of data management and their most current practical applications has become more important than ever. This book is a timely guide for anyone interested in getting up to speed with the state of the art in database systems, big data technologies, and data science. It is full of insightful examples and case studies with direct industrial relevance.' Nesime Tatbul, Intel Labs and Massachusetts Institute of Technology'It is a pleasure to study this new book on database systems. The book offers a fantastically fresh approach to database teaching. The mix of theoretical and practical contents is almost perfect, the content is up-to-date and covers the recent ones, the examples are nice, and the database testbed provides an excellent way of understanding the concepts. Coupled with the authors 'expertise, this book is an important addition to the database field.' Arnab Bhattacharya, Indian Institute of Technology, Kanpur'Principles of Database Management is my favorite textbook for teaching a course on database management. Written in a well-illustrated style, this comprehensive book covers essential topics in established data management technologies and recent discoveries in data science. With a nice balance between theory and practice, it is not only an excellent teaching medium for students taking information management and/or data analytics courses, but also a quick and valuable reference for scientists and engineers working in this area.' Chuan Xiao, Graduate School of Informatics, Nagoya University'Data science success stories and big data applications are only possible because of advances in database technology. This book provides both a broad and deep introduction to databases. It covers the different types of database systems (from relational to noSQL) and manages to bridge the gap between data modeling and the underlying basic principles. The book is highly recommended for anyone that wants to understand how modern information systems deal with ever-growing volumes of data.' Wil van der Aalst, RWTH Aachen University'The database field has been evolving for several decades and the need for updated textbooks is continuous. Now, this need is covered by this fresh book by Lemahieu, van den Broucke and Baesens. It spans from traditional topics - such as the relational model and SQL - to more recent topics – such as distributed computing with Hadoop and Spark as well as data analytics. The book can be used as an introductory text and for graduate courses.' Yannis Manolopoulos, Data Science & Engineering Lab, Aristotle University of Thessaloniki'I like the way the book covers both traditional database topics and newer material such as big data, No-SQL databases, and data quality. The coverage is just right for my course and the level of the material is very appropriate for my students. The book also has clear explanations and good examples.' Barbara Klein, University of MichiganThis book provides a unique perspective on database management and how to store, manage, and analyze small and big data. The accompanying exercises and solutions, cases, slides, and YouTube lectures turn it into an indispensable resource for anyone teaching an undergraduate or postgraduate course on the topic.' Wolfgang Ketter, Erasmus University Rotterdam'This is a very modern textbook that fills the needs of current trends without sacrificing the need to cover the required database management systems fundamentals.' George Dimitoglou, Hood College, Maryland'This book is a much needed foundational piece on data management and data science. The authors successfully integrate the fields of database technology, operations research and big data analytics, which have often been covered independently in the past. A key asset is its didactical approach that builds on a rich set of industry examples and exercises. The book is a must-read for all scholars and practitioners interested in database management, big data analytics and its applications.' Jan Mendling, Institute for Information Business, ViennaTable of ContentsPreface; Part I. Databases and Database Design: 1. Fundamental concepts of database management; 2. Architecture and categorization of DBMSs; 3. Conceptual data modeling using the (E)ER model and UML class diagram; 4. Organizational aspects of data management; Part II. Types of Database Systems: 5. Legacy databases; 6. Relational databases: the relational model; 7. Relational databases: structured query language (SQL); 8. Object oriented databases and object persistence; 9. Extended relational databases; 10. XML databases; 11. NoSQL databases; Part III. Physical Data Storage, Transaction Management, and Database Access: 12. Physical file organization and indexing; 13. Physical database organization; 14. Basics of transaction management; 15. Accessing databases and database APIs; 16. Data distribution and distributed transaction management; Part IV. Data Warehousing, Data Governance and (Big) Data Analytics: 17. Data warehousing and business intelligence; 18. Data integration, data quality and data governance; 19. Big data; 20. Analytics; Appendix A. Cases and questions; Appendix B. Using the online environment; Appendix C. Answer key to select review questions; Glossary; Index.

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Book SynopsisWhen machine learning engineers work with data sets, they may find the results aren''t as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data''s features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist''s or machine learning engineer''s toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.Trade Review'Pablo Duboue is a true grandmaster of the art and science of feature engineering. His foundational contributions to the creation of IBM Watson were a critical component of its success. Now readers can benefit from his expertise. His book provides deep insights into to how to develop, assess, combine, and enhance machine learning features. Of particular interest to advanced practitioners is his discussion of feature engineering and deep learning; there is a pervasive myth in the industry that deep learning and big data have made feature engineering obsolete, but the book explains why that is often incorrect for real-world computing applications and explains the relationship between building effective features and deep neural network architectures. The book engages with countless other basic and advanced topics in the area of machine learning and feature engineering, making it a valuable resource for machine learning practitioners of all levels of experience.' J. William Murdock, IBM'Feature engineering is the process of identifying, selecting and evaluating input variables to statistical and machine learning models for a given problem. Pablo Duboue's The Art of Feature Engineering introduces the process with rich detail from a practitioner's point of view, and adds new insights through four input data scenarios for the same prediction task. Highly recommended!' Nelson Correa, Andinum Inc.'TAoFE is a comprehensive handbook - sure to be a hit with data science practitioners. With highly accessible and didactic explanations of complex concepts, the book represents the state-of-the-art, and shows in practical terms how it applies to a wide range of real-world case studies.' Gavin Brown, University of Manchester'This book provides a large catalogue of feature manipulation techniques along with non-trivial examples to illustrate their applicability and impact on performance. It could be suitable as a textbook for an upper level undergrad or graduate text mining or multimodal data analysis class. Recent graduates starting in field data mining and text analysis will find this a useful text.' Wlodek Zadrozny, University of North CarolinaTable of ContentsPart I. Fundamentals: 1. Introduction; 2. Features, combined; 3. Features, expanded; 4. Features, reduced; 5. Advanced topics; Part II. Case Studies: 6. Graph data; 7. Timestamped data; 8. Textual data; 9. Image data; 10. Other domains.

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Book SynopsisIn discrete choice models the relationships between the independent variables and the choice probabilities are nonlinear, depending on both the value of the particular independent variable being interpreted and the values of the other independent variables. Thus, interpreting the magnitude of the effects (the substantive effects) of the independent variables on choice behavior requires the use of additional interpretative techniques. Three common techniques for interpretation are described here: first differences, marginal effects and elasticities, and odds ratios. Concepts related to these techniques are also discussed, as well as methods to account for estimation uncertainty. Interpretation of binary logits, ordered logits, multinomial and conditional logits, and mixed discrete choice models such as mixed multinomial logits and random effects logits for panel data are covered in detail. The techniques discussed here are general, and can be applied to other models with discrete dependTable of Contents1. Introduction; 2. Accounting for Statistical Uncertainty in Estimates of Substantive Effects; 3. Substantive Effects in Binary Choice Models; 4. Substantive Effects in Ordered Choice Models; 5. Substantive Effects in Multinomial Choice Models; 6. Interpretation of Mixed Discrete Choice Models; 7. Extensions.

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Table of Contents Introduction to Backup and Recovery Backup Software Physical Backup Media Virtual Backup Media New Media Technologies Software Architectures: CommVault Software Architectures: NetBackup Application Backup Strategies Putting It All Together: Sample Backup Environments Monitoring and Reporting Summary

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Book SynopsisThis work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space.Table of ContentsVector Quantisation and Topology-Based Graph RepresentationGraph-Based Clustering AlgorithmsGraph-Based Visualisation of High-Dimensional Data

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Book SynopsisThis bestseller can help anyone whose role is to try to find specific causes for failures.It provides detailed steps for solving problems, focusing more heavily on the analytical process involved in finding the actual causes of problems. It does this using figures, diagrams, and tools useful for helping to make our thinking visible. This increases our ability to see what is truly significant and to better identify errors in our thinking. In the sections on finding root causes, this second edition now includes more examples on the use of multi-vari charts; how thought experiments can help guide data interpretation; how to enhance the value of the data collection process; cautions for analyzing data; and what to do if one can''t find the causes. In its guidance on solution identification, biomimicry and TRIZ have been added as potential solution identification techniques. In addition, the appendices have been revised to include: an expanded breakdown of the 7 M''s, which includes more than 50 specific possible causes; forms for tracking causes and solutions, which can help maintain alignment of actions; techniques for how to enhance the interview process; and example responses to problem situations that the reader can analyze for appropriateness.

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Book SynopsisCluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gérard Govaert is Professor at the University of Technology of Compiègne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization approach is considered and algorithms based on update rules are described. Links with numerical and probabilistic approaches are established. A combination of algorithms are proposed and evaluated on simulated and real data. Chapter 5 considers a co-clustering or bi-clustering as the search for coherent co-clusters in biological terms or the extraction of co-clusters under conditions. Classical algorithms will be described and evaluated on simulated and real data. Different indices to evaluate the quality of coclusters are noted and used in numerical experiments.Table of ContentsAcknowledgment xi Introduction xiii I.1. Types and representation of data xiii I.1.1. Binary data xiv I.1.2. Categorical data xiv I.1.3. Continuous data xv I.1.4. Contingency table xvii I.1.5. Data representations xix I.2. Simultaneous analysis xx I.2.1. Data analysis xx I.2.2. Co-clustering xxii I.2.3. Applications xxiii I.3. Notation xxvii I.4. Different approaches xxviii I.4.1. Two-mode partitioning xxviii I.4.2. Two-mode hierarchical clustering xxxvii I.4.3. Direct or block clustering xxxix I.4.4. Biclustering xxxix I.4.5. Other structures and other aims xliv I.5. Model-based co-clustering xlvi I.6. Outline xlix Chapter 1. Cluster Analysis 1 1.1. Introduction 1 1.2. Miscellaneous clustering methods 4 1.2.1. Hierarchical approach 4 1.2.2. The k-means algorithm 5 1.2.3. Other approaches 7 1.3. Model-based clustering and the mixture model 11 1.4. EM algorithm 15 1.4.1. Complete data and complete-data likelihood 16 1.4.2. Principle 17 1.4.3. Application to mixture models 18 1.4.4. Properties 19 1.4.5. EM: an alternating optimization algorithm 19 1.5. Clustering and the mixture model 20 1.5.1. The two approaches 20 1.5.2. Classification likelihood 21 1.5.3. The CEM algorithm 22 1.5.4. Comparison of the two approaches 22 1.5.5. Fuzzy clustering 24 1.6. Gaussian mixture model 26 1.6.1. The model 26 1.6.2. CEM algorithm 28 1.6.3. Spherical form, identical proportions and volumes 29 1.6.4. Spherical form, identical proportions but differing volumes 30 1.6.5. Identical covariance matrices and proportions 31 1.7. Binary data 32 1.7.1. Binary mixture model 32 1.7.2. Parsimonious model 33 1.7.3. Examples of application 35 1.8. Categorical variables 36 1.8.1. Multinomial mixture model 36 1.8.2. Parsimonious model 38 1.9. Contingency tables 41 1.9.1. MNDKI2 algorithm 41 1.9.2. Model-based approach 43 1.9.3. Illustration 47 1.10. Implementation 49 1.10.1. Choice of model and of the number of classes 51 1.10.2. Strategies for use 51 1.10.3. Extension to particular situations 52 1.11. Conclusion 53 Chapter 2. Model-Based Co-Clustering 55 2.1. Metric approach 55 2.2. Probabilistic models 57 2.3. Latent block model 59 2.3.1. Definition 59 2.3.2. Link with the mixture model 61 2.3.3. Log-likelihoods 62 2.3.4. A complex model 63 2.4. Maximum likelihood estimation and algorithms 67 2.4.1. Variational EM approach 69 2.4.2. Classification EM approach 72 2.4.3. Stochastic EM-Gibbs approach 73 2.5. Bayesian approach 75 2.6. Conclusion and miscellaneous developments 76 Chapter 3. Co-Clustering of Binary and Categorical Data 79 3.1. Example and notation 80 3.2. Metric approach 82 3.3. Bernoulli latent block model and algorithms 84 3.3.1. The model 84 3.3.2. Model identifiability 85 3.3.3. Binary LBVEM and LBCEM algorithms 86 3.4. Parsimonious Bernoulli LBMs 90 3.5. Categorical data 91 3.6. Bayesian inference 93 3.7. Model selection 96 3.7.1. The integrated completed log-likelihood (ICL) 96 3.7.2. Penalized information criteria 97 3.8. Illustrative experiments 98 3.8.1. Townships 98 3.8.2. Mero 101 3.9. Conclusion 105 Chapter 4. Co-Clustering of Contingency Tables 107 4.1. Measures of association 108 4.1.1. Phi-squared coefficient 109 4.1.2. Mutual information 111 4.2. Contingency table associated with a couple of partitions 113 4.2.1. Associated distributions 113 4.2.2. Associated measures of association 116 4.3. Co-clustering of contingency table 119 4.3.1. Two equivalent approaches 119 4.3.2. Parameter modification of criteria 121 4.3.3. Co-clustering with the phi-squared coefficient 124 4.3.4. Co-clustering with the mutual information 129 4.4. Model-based co-clustering 131 4.4.1. Block model for contingency tables 133 4.4.2. Poisson latent block model 137 4.4.3. Poisson LBVEM and LBCEM algorithms 138 4.5. Comparison of all algorithms 140 4.5.1. CROKI2 versus CROINFO 142 4.5.2. CROINFO versus Poisson LBCEM 142 4.5.3. Poisson LBVEM versus Poisson LBCEM 144 4.5.4. Behavior of CROKI2, CROINFO, LBCEM and LBVEM 147 4.6. Conclusion 149 Chapter 5. Co-Clustering of Continuous Data 151 5.1. Metric approach 152 5.1.1. Measure of information 153 5.1.2. Summarized data associated with partitions 153 5.1.3. Objective function 156 5.1.4. CROEUC algorithm 157 5.2. Gaussian latent block model 159 5.2.1. The model 159 5.2.2. Gaussian LBVEM and LBCEM algorithms 160 5.2.3. Parsimonious Gaussian latent block models 161 5.3. Illustrative example 163 5.4. Gaussian block mixture model 168 5.4.1. The model 169 5.4.2. GBEM algorithm 170 5.5. Numerical experiments 173 5.5.1. GBEM versus CROEUC and EM 174 5.5.2. Effect of the size of data 175 5.6. Conclusion 175 Bibliography 177 Index 199

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Book SynopsisThis text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features:· An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc.· Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning.New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering· Restructured to make the logics more straightforward and sections self-containedCore Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners. Trade Review“This book provides a clear overview of the data analysis process, the different types of statistical techniques employed for data analysis, and their role and purpose. … There is good use of a variety of examples to demonstrate how the different techniques are applied in practice. The book’s main purpose would be as a textbook for undergraduate students, or a reference book for data analysts.” (Mark Taylor, Computing Reviews, May 5, 2022)Table of Contents

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book’s primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the “spin-net,” as a dynamic form of auto-associative memory.Table of ContentsAn Introduction.- Artificial Neural Networks.- Auto-Contractive Maps.- Visualization of Auto-CM Output.- Dataset Transformations and Auto-CM.- Comparison of Auto-CM to Various Other Data Understanding Approaches.

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Book SynopsisThe three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: classification and supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining unstructured and semi-structured data; behavioral data mining; visual data mining; and knowledge graph and interpretable data mining.

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Book SynopsisThis book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection. Trade Review“The book discusses mathematical theories of machine learning. … The book is very technically written and it is addressed to professionals in the field.” (Smaranda Belciug, zbMATH 1422.68003, 2019)Table of ContentsChapter 1. Introduction.- Chapter 2. General Framework of Mathematics.- Chapter 3. Problem Formulation.- Chapter 4. Development of Novel Techniques of CoCoSSC Method.- Chapter 5. Further Discussions of the Proposed Method.- Chapter 6. Related Work on Geometry of Non-Convex Programs.- Chapter 7. Gradient Descent Converges to Minimizers.- Chapter 8. A Conservation Law Method Based on Optimization.- Chapter 9. Improved Sample Complexity in Sparse Subspace Clustering with Noisy and Missing Observations.- Chapter 10. Online Discovery for Stable and Grouping Causalities in Multi-Variate Time Series.- Chapter 11. Conclusion.

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Book SynopsisThis book constitutes the proceedings of the 15th International Conference on Intelligent Tutoring Systems, ITS 2019, held in Kingston, Jamaica, in June 2019. The 14 full papers and 13 short papers presented in this volume were carefully reviewed and selected from 42 submissions. In the back matter of the volume 4 poster papers are included. They deal with the use of advanced computer technologies and interdisciplinary research for enabling, supporting, and enhancing human learning.Table of ContentsA Learning Early-warning Model Based on Knowledge Points.- Adaptive Learning Spaces with Context-Awareness.- Agents’ Cognitive vs. Socio-affective Support in Response to Learner’s Confusion.- An Adaptive Approach to Provide Feedback for Students in Programming Problem Solving.- Analysis and Prediction of Student Emotions While Doing Programming Exercises.- Analyzing Best Hints for a Programming IST.- Analyzing the Group Formation Process in Intelligent Tutoring Systems.- Analyzing the usage of the classical ITS software architecture and refining it.- Assessing Students’ Clinical Reasoning using Gaze and EEG Features.- Computer-Aided Intervention for Reading Comprehension Disabilities.- Conceptualization of IMS that Estimates Learners’ Mental States from Learners’ Physiological Information Using Deep Neural Network Algorithm.- Data-Driven Student Clusters Based on Online Learning Behavior in a Flipped Classroom with an Intelligent Tutoring System.- Decision Support for an Adversarial Game Environment using Automatic Hint Generation.- Detecting Collaborative Learning through Emotions: An Investigation using Facial Expression Recognition.- Fact Checking Misinformation Using Recommendations from Emotional Pedagogical Agents.- Intelligent On-line Exam Management and Evaluation System.- Learning by Arguing in Argument-Based Machine Learning Framework.- Model for data analysis process and its relationship to the hypothesis-driven and data-driven research approaches.- On the discovery of educational patterns using biclustering.- Parent-Child Interaction in Children's Learning How to Use a New Application.- Patterns of Collaboration Dialogue Acts in Typed-Chat Group Problem-Solving.- PKULAE: A Learning Attitude Evaluation Method Based on Learning Behavior.- Predicting MOOCs Dropout Using only two easily obtainable Features from the First Week’s Activities.- Predicting subjective enjoyment of aspects of a videogame from psychophysiological measures of arousal and valence.- Providing the Option to Skip Feedback – A Reproducibility Study.- Reducing Annotation Effort in Automatic Essay Evaluation Using Locality Sensitive Hashing.- Representing and Evaluating Strategies for Solving Parsons Puzzles.- Testing the Robustness of Inquiry Practices once Scaffolding is Removed.- Toward Real-Time System Adaptation using Excitement Detection from Eye Tracking.- Towards Predicting Attention And Workload During Math Problem Solving.- Using a Simulator to Choose the Best Hints in a Reinforcement Learning-Based Multimodal ITS.

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Book SynopsisThis book constitutes the proceedings of the 22nd International Conference on Discovery Science, DS 2019, held in Split, Coratia, in October 2019. The 21 full and 19 short papers presented together with 3 abstracts of invited talks in this volume were carefully reviewed and selected from 63 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Advanced Machine Learning; Applications; Data and Knowledge Representation; Feature Importance; Interpretable Machine Learning; Networks; Pattern Discovery; and Time Series.Table of ContentsAdvanced Machine Learning.- Applications.- Data and Knowledge Representation.- Feature Importance.- Interpretable Machine Learning.- Networks.- Pattern Discovery.- Time Series.

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Book SynopsisThis open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.Table of ContentsMultivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder.- Dual Sequential Variational Autoencoders for Fraud Detection.- A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks.- Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams.- GraphMDL: Graph Pattern Selection Based on Minimum Description Length.- Towards Content Sensitivity Analysis.- Gibbs Sampling Subjectively Interesting Tiles.- Even Faster Exact k-Means Clustering.- Ising-Based Consensus Clustering on Special Purpose Hardware.- Transfer Learning by Learning Projections from Target to Source.- Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs.- Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces.- Vouw: Geometric Pattern Mining Using the MDL Principle.- A Consensus Approach to Improve NMF Document Clustering.- Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams.- Widening for MDL-Based Retail Signature Discovery.- Addressing the Resolution Limit and the Field of View Limit in Community Mining.- Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics.- Adversarial Attacks Hidden in Plain Sight.- Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code.- Overlapping Hierarchical Clustering (OHC).- Digital Footprints of International Migration on Twitter.- Percolation-Based Detection of Anomalous Subgraphs in Complex Networks.- A Late-Fusion Approach to Community Detection in Attributed Networks.- Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction.- A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization.- Actionable Subgroup Discovery and Urban Farm Optimization.- AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model.- Detection of Derivative Discontinuities in Observational Data.- Improving Prediction with Causal Probabilistic Variables.- DO-U-Net for Segmentation and Counting.- Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media.- Event Recognition Based on Classification of Generated Image Captions.- Human-to-AI Coach: Improving Human Inputs to AI Systems.- Aleatoric and Epistemic Uncertainty with Random Forests.- Master your Metrics with Calibration.- Supervised Phrase-Boundary Embeddings.- Predicting Remaining Useful Life with Similarity-Based Priors.- Orometric Methods in Bounded Metric Data.- Interpretable Neuron Structuring with Graph Spectral Regularization.- Comparing the Preservation of Network Properties by Graph Embeddings.- Making Learners (More) Monotone.- Combining Machine Learning and Simulation to a Hybrid Modelling Approach.- LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.- Angle-Based Crowding Degree Estimation for Many-Objective Optimization.

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Book SynopsisThis textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail.The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified.The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.Table of ContentsPart I: Introduction to Visual Analytics in Data Science.- 1. Introduction to Visual Analytics by an Example.- 2. General Concepts.- 3. Principles of Interactive Visualisation.- 4. Computational Techniques in Visual Analytics.- Part II: Visual Analytics along the Data Science Workflow.- 5. Visual Analytics for Investigating and Processing Data.- 6. Visual Analytics for Understanding Multiple Attributes.- 7. Visual Analytics for Understanding Relationships between Entities.- 8. Visual Analytics for Understanding Temporal Distributions and Variations.- 9. Visual Analytics for Understanding Spatial Distributions and Spatial Variation.- 10. Visual Analytics for Understanding Phenomena in Space and Time.- 11. Visual Analytics for Understanding Texts.- 12. Visual Analytics for Understanding Images and Video.- 13. Computational Modelling with Visual Analytics.- 14. Conclusion.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on Similarity Search and Applications, SISAP 2020, held in Copenhagen, Denmark, in September/October 2020. The conference was held virtually due to the COVID-19 pandemic.The 19 full papers presented together with 12 short and 2 doctoral symposium papers were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections named: scalable similarity search; similarity measures, search, and indexing; high-dimensional data and intrinsic dimensionality; clustering; artificial intelligence and similarity; demo and position papers; and doctoral symposium.Table of ContentsScalable Similarity Search.- Accelerating Metric Filtering by Improving Bounds on Estimated Distances.- Differentially Private Sketches for Jaccard Similarity Estimation.- Pivot Selection for Narrow Sketches by Optimization Algorithms.- mmLSH: A Practical and Efficient Technique for Processing Approximate Nearest Neighbor Queries on Multimedia Data.- Parallelizing Filter-Verification based Exact Set Similarity Joins on Multicores.- Similarity Search with Tensor Core Units.- On the Problem of p1 in Locality-Sensitive Hashing.- Similarity Measures, Search, and Indexing.- Confirmation Sampling for Exact Nearest Neighbor Search.- Optimal Metric Search Is Equivalent to the Minimum Dominating Set Problem.- Metrics and Ambits and Sprawls, Oh My: Another Tutorial on Metric Indexing.- Some branches may bear rotten fruits: Diversity browsing VP-Trees.- Continuous Similarity Search for Evolving Database.- Taking advantage of highly-correlated attributes in similarity queries with missing values.- Similarity Between Points in Metric Measure Spaces.- High-dimensional Data and Intrinsic Dimensionality.- GTT: Guiding the Tensor Train Decomposition.- Noise Adaptive Tensor Train Decomposition for Low-Rank Embedding of Noisy Data.- ABID: Angle Based Intrinsic Dimensionality.- Sampled Angles in High-Dimensional Spaces.- Local Intrinsic Dimensionality III: Density and Similarity.- Analysing Indexability of Intrinsically High-dimensional Data using TriGen.- Reverse k-Nearest Neighbors Centrality Measures and Local Intrinsic Dimension.- Clustering.- BETULA: Numerically Stable CF-Trees for BIRCH Clustering.- Using a Set of Triangle Inequalities to Accelerate K-means Clustering.- Angle-Based Clustering.- Artificial Intelligence and Similarity.- Improving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors.- SIR: Similar Image Retrieval for Product Search in E-Commerce.- Cross-Resolution deep features based Image Search.- Learning Distance Estimators from Pivoted Embeddings of Metric Objects.- Demo and Position Papers.- Visualizer of Dataset Similarity using Knowledge Graph.- vitrivr-explore: Guided Multimedia Collection Exploration for Ad-hoc Video Search.- Running experiments with confidence and sanity.- Doctoral Symposium.- Temporal Similarity of Trajectories in Graphs.- Relational Visual-Textual Information Retrieval.

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Book SynopsisThis open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. Table of Contents

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Book SynopsisThis book provides information on data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart cities. This book aims to attract works on multidisciplinary research spanning across the computer science and engineering, environmental studies, services, urban planning and development, social sciences and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative solutions and big data-powered applications to cope with the real world challenges for building smart cities.Table of Contents1. Smart City Ecosystem – An Introduction.- 2. Datafication for secured smart cities.- 3. Secured big data infrastructure services.- 4. Intelligent infrastructure of secured smart cities.- 5. Cyber-physical systems for secured smart cities.- 6. Blockchain for smart cities.- 7. Context-aware security and privacy of smart cities.- 7. Privacy and social Issues in smart cities.- 8. Sensor and RFID applications of smart cities.- 9. Advanced data mining for secured smart cities.- 10. Big data for secured smart cities.- 11. Data analytics tools and technologies for smart cities.- 12. Machine learning and AI for secured smart cities.

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Book SynopsisThis book constitutes revised selected papers from the International Workshops held at the Second International Conference on Process Mining, ICPM 2020, which took place during October 4-9, 2020. The conference was planned to take place in Padua, Italy, but had to be held online due to the COVID-19 pandemic.The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 29 papers included in this volume were carefully reviewed and selected from 59 submissions. They stem from the following workshops: 1st International Workshop on Event Data and Behavioral Analytics (EDBA) 1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 1st International Workshop on Streaming Analytics for Process Mining (SA4PM'20) 5th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 3rd International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 1st International Workshop on Trust and Privacy in Process Analytics (TPPA) Table of Contents1st International Workshop on Event Data and Behavioral Analytics (EDBA).- Visually Representing History Dependencies in Event Logs.- Analysis of Business Process Batching using Causal Event Models.- Process Procespecting to Improve Renewable Energy Interconnection Queues: A Case Study.- Automated Discovery of Process Models with True Concurrency and Inclusive Choices.- A Novel Approach to Discover Switch Behaviours in Process Mining.- Process Model Discovery from Sensor Event Data.- Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study.- 1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM).- Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-based Approach.- Time Matters:Time-Aware LSTMs for Predictive Business Process Monitoring.- A preliminary study on the application of Reinforcement Learning for Predictive Process Monitoring.- An Alignment Cost-Based Classi cation of Log Traces Using Machine-Learning.- Improving the Extraction of Process Annotations from Text with Inter-Sentence Analysis.- Case2vec: Advances in Representation Learning for Business Processes.- Supervised Conformance Checking using Recurrent Neural Network Classifiers.- 1st International Workshop on Streaming Analytics for Process Mining (SA4PM'20).- Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events.- Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation.- OTOSO: Online Trace Ordering for Structural Overviews.- Performance Skyline: Inferring Process Performance Models from Interval Events.- 5th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2020).- Alignment Approximation for Process Trees.- Stochastic Process Discovery By Weight Estimation.- Graph-based Process Mining.- Third International Workshop on Process-Oriented Data Science for Healthcare (PODS4H).- A Process Mining approach to statistical analysis: application to a real-world advanced melanoma dataset.- Process Mining of Disease Trajectories in MIMIC-III: A Case Study.- The Need for Interactive Data-Driven Process Simulation in Healthcare: A Case Study.- Process mining on the extended event log to analyse the system usage during healthcare processes (Case study: the GP Tab usage during chemotherapy treatments).- Process Mining on FHIR - An Open Standards-Based Process Analytics Suite for Healthcare.- Deriving a sophisticated clinical pathway based on patient conditions from electronic health record data.- Exploration on How Global Warming Affects Emergency Services.- 1st Workshop on Trust and Privacy in Process Analytics (TPPA).- Towards Quantifying Privacy in Process Mining.

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Book SynopsisThis volume gathers peer-reviewed contributions that address a wide range of recent developments in the methodology and applications of data analysis and classification tools in micro and macroeconomic problems. The papers were originally presented at the 29th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2020, held in Sopot, Poland, September 7–9, 2020. Providing a balance between methodological contributions and empirical papers, the book is divided into five parts focusing on methodology, finance, economics, social issues and applications dealing with COVID-19 data. It is aimed at a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.Table of ContentsPart 1: Methodology Chapter 1 - Evaluation of Two-Step Spectral Clustering Algorithm for Large Untypical Data Sets (Andrzej Dudek) Chapter 2 - Determining the Number of Groups in Cluster Analysis Using Classical Indexes and Stability Measures – Comparison of Results (Dorota Rozmus)Chapter 3 - Identification of the Words Most Frequently Used by Different Generations of Twitter Users (Agata Majkowska, Kamila Migdał-Najman, Krzysztof Najman and Katarzyna Raca)Chapter 4 - Classification Algorithms Applications for Information Security on the Internet: a Review (Michał Bryś)Chapter 5 - Outlier Detection with the Use of Isolation Forests (Krzysztof Najman and Krystian Zieliński)Part 2: Application in FinanceChapter 6 - Propositions of Transformations of Asymmetrical Nominants into Stimulants on the Example of Chosen Financial Ratios ( Barbara Batóg and Katarzyna Wawrzyniak)Chapter 7 - Gini Regression in The Capital Investment Risk Assessment – Sensitivity Risk Measures in Portfolio Analysis (Grażyna Trzpiot).- Part 3: Application in EconomicsChapter 8 - Enterprise Dark Data (Katarzyna Raca)Chapter 9 - The Significance of Medical Science Issues in Research Papers Published in the Field of Economics (Urszula Cieraszewska, Monika Hamerska, Paweł Lula and Marcela Zembura)Chapter 10 - Application of Duration Analysis Methods in the Study of the Exit of a Real Estate Sale Offer from the Offer Database System (Ewa Putek-Szeląg, Anna Gdakowicz)Chapter 11 - Is Society Ready for Long-Term Investments? – Profiles of Electricity Users in Silesia (Sylwia Słupik and Joanna Trzęsiok) Chapter 12 - The Use of the Spatial Taxonomic Measure of Development to Assess the Tourist Attractiveness of Districts of the Lesser Poland Province(Jacek Wolak).- Part 4: Application in Social Issues Chapter 13 - Models of Competing Events in Assessing the Effects of the Transition of Unemployed People Between the States of Registration and De-registration (Beata Bieszk-Stolorz).- Chapter 14 - Direct Adjusted Survival Probabilities in the Analysis of Finding a Job by the Unemployed Depending on Their Individual Characteristics(Wioletta Grzenda)Chapter 15 - Europe 2020 Strategy – Objective Evaluation of Realization and Subjective Assessment by Seniors as Beneficiaries of Social Assumptions (Klaudia Przybysz, Agnieszka Stanimir and Marta Wasiak)Chapter 16 - Do Seniors Get to the Disco by Bike or in a Taxi? – Classification of Seniors According to Their Preferred Means of Transport (Joanna Kos-Łabędowicz and Joanna Trzęsiok)Part 5: Application with COVID-19 Data Chapter 17 - The Impact of the COVID-19 Pandemic on the Economies of European Countries in the Period January-September 2020 Based on Economic Indicators (Ewelina Nojszewska and Agata Sielska)Chapter 18 - Modelling the Risk of Foreign Divestment in the Visegrad Group Countries During the COVID-19 Pandemic (Marcin Salamaga) Chapter 19 - Analysis of COVID-19 Dynamics in EU Countries Using the Dynamic Time Warping Method and ARIMA Models (Joanna Landmesser).

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Book SynopsisThis book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society.Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.Table of ContentsStudy on Discrete Action Sequences using Deep Emotional Intelligence.- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms.- A High Security Framework through Human Brain using Algo Mixture Model Deep Learning Algorithm.- Knowledge Framework for Deep Learning: Congenital Heart Disease.- Computing System and Machine Learning.- Automatic Image Segmentation by Ranking based SVM in Convolutional Neural Network on Diabetic Fundus Image.

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Book SynopsisThe new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life examples of how business analytics have been used in various aspects of organizations to solve issues or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.Table of ContentsChapter 1 Introduction to Predictive Analytics1 1.1 Predictive Analytics in Action2 1.2 Analytics Landscape8 1.3 Analytics 1.3.2 Predictive Analytics 1.4 Regression Analysis 1.5 Machine Learning Techniques 1.6 Predictive Analytics Model 1.7 Opportunities in Analytics 1.8 Introduction to the Automobile Insurance Claim Fraud Example 1.9 Chapter Summary References Chapter 239 Know Your Data – Data Preparation39 2.1 Classification of Data40 2.1.1 Qualitative versus Quantitative 2.1.2 Scales of Measurement 2.2. Data Preparation Methods. 2.2.1 Inconsistent Formats 2.2.2 Missing Data 2.2.3 Outliers 2.2.4 Other Data Cleansing Considerations 2.3 Data Sets and Data Partitioning 2.4 SAS Enterprise Miner™ Model Components 2.4.1 Step 1. Create Three of the Model Components 2.4.2 Step 2. Import an Excel File and Save as a SAS File 2.4.3 Step 3. Create the Data Source 2.4.4 Step 4. Partition the Data Source 2.4.5 Step 5 Data Exploration 2.4.6 Step 6 Missing Data 2.4.7 Step 7. Handling Outliers 2.4.8 Step 8. Categorical Variables with Too Many Levels 2.5 Chapter Summary References Chapter 35 What do Descriptive Statistics Tell Us 3.1 Descriptive Analytics 3.2 The Role of the Mean, Median and Mode 3.3 Variance and Distribution 3.4 The Shape of the Distribution 3.4.2 Kurtosis 3.5 Covariance and Correlation 3.6 Variable Reduction 3.6.1 Variable Clustering 3.6.2 Principal Component Analysis 3.7 Hypothesis Testing2 3.8 Analysis of Variance (ANOVA)5 3.9 Chi Square6 3. Fit Statistics8 3. Stochastic Models9 3.12 Chapter Summary1 References2 Chapter 4 Predictive Models Using Regression5 4.1 Regression6 4.1.1 Classical assumptions7 4.2 Ordinary Least Squares8 4.3 Simple Linear Regression8 4.3.1 Determining Relationship Between Two Variables9 4.3.2 Line of Best Fit and Simple Linear Regression Equation9 4.4 Multiple Linear Regression1 4.4.1 Metrics to Evaluate the Strength of the Regression Line2 4.3.2 Best-fit model3 4.3.3 Selection of Variables in Regression3 4.5 Principal Component Regression5 4.5.1 Principal Component Analysis Revisited5 4.5.2 Principal Component Regression6 4.6 Partial Least Squares6 4.7 Logistic Regression7 4.7.1 Binary Logistic Regression8 4.7.2 Examination of Coefficients1 4.7.3 Multinomial Logistic Regression3 4.7.4 Ordinal Logistic Regression3 4.8 Implementation of Regression in SAS Enterprise Miner™3 4.8.1 Regression Node Train Properties: Class Targets4 4.8.2 Regression Node Train Properties: Model Options5 4.8.3 Regression Node Train Properties: Model Selection6 4.9 Implementation of Two-Factor Interaction and Polynomial Terms8 4.9.1 Regression Node Train Properties: Equation8 4. DMINE Regression in SAS Enterprise Miner™0 4..1 DMINE Properties0 4..2 DMINE Results2 4. Partial Least Squares Regression in SAS Enterprise Miner™4 4..1 Partial Least Squares Properties4 4..2 Partial Least Squares Results7 4. Least Angles Regression in SAS Enterprise Miner™9 4..1 Least Angle Regression Properties0 4..2 Least Angles Regression Results1 4. Other Forms of Regression4 4. Chapter Summary6 References9 Chapter 5 The Second of the Big Three – Decision Trees1 5.1 What is a Decision Tree?2 5.2 Creating a Decision Tree4 5.3 Data Partitions and Decision Trees6 5.4 Creating a Decision Tree Using SAS Enterprise Miner™9 The key properties include:5 Subtree Properties5 5.4.1 Overfitting1 5.5 Creating an Interactive Decision Tree using SAS Enterprise Miner ™1 5.6 Creating a Maximal Decision Tree using SAS Enterprise Miner ™6 5.7 Chapter Summary9 References1 Chapter 6 The Third of the Big Three - Neural Networks3 6.1 What is a Neural Network?4 6.2 History of Neural Networks6 6.3 Components of a Neural Network8 6.4 Neural Network Architectures2 6.5 Training a Neural Network5 6.6 Radial Basis Function Neural Networks6 6.7 Creating a Neural Network using SAS Enterprise MinerÔ7 6.8 Using SAS Enterprise MinerÔ to Automatically Generate a Neural Network0 6.9 Explaining a Neural Network6 6. Chapter Summary0 References3 Chapter 7 Model Comparisons and Scoring5 7.1 Beyond the Big 7.2 Gradient Boosting6 7.3 Ensemble Models0 7.4 Random Forests2 7.6 Two-Stage Model8 7.7 Comparing Predictive Models0 7.7.1 Evaluating Fit Statistics – Which Model Do We Use?2 7.8 Using Historical Data to Predict the Future – Scoring5 7.8.1 Analyzing and Reporting Results8 7.8.2 Save Data Node9 7.8.3 Reporter Node0 7.9 The Importance of Predictive Analytics2 7.9.1 What Should We Expect for Predictive Analytics in the Future?3 7. Chapter Summary4 References6 Chapter 8 finding Associations in Data through Cluster Analysis9 8.1 Applications and Uses of Cluster Analysis9 8.2 Types of Clustering Techniques0 8.3 Hierarchical Clustering1 8.3.1 Agglomerative Clustering1 8.3.2 Divisive Clustering1 8.3.3 Agglomerative vs Divisive Clustering6 8.4 Non-hierarchical clustering7 8.4.1 K-means Clustering7 8.4.2 Initial Centroid Selection1 8.4.3 Determining the Number of Clusters2 8.4.4 Evaluating your clusters5 8.5 Hierarchical vs Nonhierarchical6 8.6 Cluster Analysis using SAS Enterprise Miner™6 8.6.1 Cluster Node7 8.6.2 Additional Key Properties of the Cluster Node8 8.7 Applying Cluster Analysis to the Insurance Claim Fraud Data Set9 8.8 Chapter Summary8 References9 9.1 What is Text Analytics?1 9.2 Information Retrieval2 9.3 Text Parsing5 9.4 Zipf’s Law8 9.5 Text Filter9 9.6 Text Cluster1 9.7 Text Topic4 9.8 Text Rule Builder7 9.9 Text Profile8 9. Chapter Summary9 Discussion Questions0 References1 Appendix A3 Data Dictionary for the Automobile Insurance Claim Fraud Data Example3 Appendix B5 Can you Predict the Money Laundering Cases?5 B.1 Introduction5 B.2. Business Problem8 B.3. Analyze Data9 B.4. Development and Optimization of a Best Fit Model2 B.5. Final Report3 References4

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Book SynopsisThis book focuses on a basic theoretical framework dealing with the problems, solutions, and applications of text mining and its various facets in a very practical form of case studies, use cases, and stories. The book contains 11 chapters with 14 case studies showing 8 different text mining and visualization approaches, and 17 stories. In addition, both a website and a Github account are also maintained for the book. They contain the code, data, and notebooks for the case studies; a summary of all the stories shared by the librarians/faculty; and hyperlinks to open an interactive virtual RStudio/Jupyter Notebook environment. The interactive virtual environment runs case studies based on the R programming language for hands-on practice in the cloud without installing any software. From understanding different types and forms of data to case studies showing the application of each text mining approaches on data retrieved from various resources, this book is a must-read for all library professionals interested in text mining and its application in libraries. Additionally, this book will also be helpful to archivists, digital curators, or any other humanities and social science professionals who want to understand the basic theory behind text data, text mining, and various tools and techniques available to solve and visualize their research problems. Table of Contents1. The Computational Library.- 2. Text Data and Where to Find Them?.- 3. Text Pre-Processing.- 4. Topic Modeling.- 5. Network Text Analysis.- 6. Burst Detection.- 7. Sentiment Analysis.- 8. Predictive Modeling.- 9. Information Visualization.- 10. Tools and Techniques for Text Mining and Visualization.- 11. Text Data and Mining Ethics.

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Book SynopsisThis book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering. Table of ContentsChapter 1. Introduction to Federated Learning.- Chapter 2. Federated Learning for IoT Devices.- Chapter 3. Personalized Federated Learning.- Chapter 4. Federated Learning for an IoT Application.- Chapter 5. Some observations on the behaviour of Federated Learning.- Chapter 6. Federated Learning with Cooperating Devices: A Consensus Approach.- Chapter 7. A prospective study of federated machine learning in medical image fusion.- Chapter 8. Communication-Efficient Federated Learning in Wireless-Edge Architecture.- Chapter 9. Towards Ubiquitous AI in 6G with Federated Learning.- Chapter 10. Federated Learning using Tensor Flow.- Chapter 11. Cyber Security and privacy of Connected and Automated Vehicles (CAVs) based Federated Learning: Challenges, Opportunities and Open Issues.- Chapter 12. Security Issues & Solutions for Healthcare Informatics.- Chapter 13. Federated Learning: Challenges, Methods, and Future Directions.- Chapter 14. Quantum Federated Learning for Wireless Communications.- Chapter 15. Federated machine learning with data mining in health care.- Chapter 16. Federated Learning for data mining in Healthcare.

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Book SynopsisText Mining with MATLAB® provides a comprehensive introduction to text mining using MATLAB. It is designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications. The book is structured in three main parts: The first part, Fundamentals, introduces basic procedures and methods for manipulating and operating with text within the MATLAB programming environment. The second part of the book, Mathematical Models, is devoted to motivating, introducing, and explaining the two main paradigms of mathematical models most commonly used for representing text data: the statistical and the geometrical approach. Eventually, the third part of the book, Techniques and Applications, addresses general problems in text mining and natural language processing applications such as document categorization, document search, content analysis, summarization, question answering, and conversational systems. This second edition includes updates in line with the recently released “Text Analytics Toolbox” within the MATLAB product and introduces three new chapters and six new sections in existing ones. All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation. Table of Contents1. Introduction.- PART I: FUNDAMENTALS.- 2. Handling Text Data.- 3. Regular Expressions.- 4. Basic Operations with Strings.- 5. Reading and Writing Files.- 6. The Structure of Language.- PART II: MATHEMATICAL MODELS.- 7. Basic Corpus Statistics.- 8. Statistical Models.- 9. Geometrical Models.- 10. Dimensionality Reduction.- PART III: METHODS AND APPLICATIONS.- 11. Document Categorization.- 12. Document Search.- 13. Content Analysis.- 14. Keyword Extraction and Summarization.- 15. Question Answering and Dialogue.

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Book SynopsisThis book constitutes the refereed proceedings of the 6th International Conference on Belief Functions, BELIEF 2021, held in Shanghai, China, in October 2021. The 30 full papers presented in this book were carefully selected and reviewed from 37 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.Table of ContentsClustering.- Transfer learning.- Classification.- Statistical inference and learning.- Deep learning.- Conflict, inconsistency and specificity.- Information fusion.- Elicitation.- Algorithms and computation.

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Book SynopsisThis book constitutes the proceedings of the 24th International Conference on Discovery Science, DS 2021, which took place virtually during October 11-13, 2021.The 36 papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: applications; classification; data streams; graph and network mining; machine learning for COVID-19; neural networks and deep learning; preferences and recommender systems; representation learning and feature selection; responsible artificial intelligence; and spatial, temporal and spatiotemporal data. Table of ContentsApplications.- Automated Grading of Exam Responses: An Extensive Classification Benchmark.- Automatic human-like detection of code smells.- HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM.- Predicting reach to find persuadable customers: improving uplift models for churn prevention.- Classification.- A Semi-Supervised Framework for Misinformation Detection.- An Analysis of Performance Metrics for Imbalanced Classification.- Combining Predictions under Uncertainty: The Case of Random Decision Trees.- Shapley-Value Data Valuation for Semi-Supervised Learning.- Data streams.- A Network Intrusion Detection System for Concept Drifting Network Traffic Data.- Incremental k-Nearest Neighbors Using Reservoir Sampling for Data Streams.- Statistical Analysis of Pairwise Connectivity.- Graph and Network Mining.- FHA: Fast Heuristic Attack against Graph Convolutional Networks.- Ranking Structured Objects with Graph Neural Networks.- Machine Learning for COVID-19.- Knowledge discovery of the delays experienced in reporting covid19 confirmed positive cases using time to event models.- Multi-Scale Sentiment Analysis of Location-Enriched COVID-19 Arabic Social Data.- Prioritization of COVID-19 literature via unsupervised keyphrase extraction and document representation learning.- Sentiment Nowcasting during the COVID-19 Pandemic.- Neural Networks and Deep Learning.- A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data.- Calibrated Resampling for Imbalance and Long-Tails in Deep learning.- Consensus Based Vertically Partitioned Multi-Layer Perceptrons for Edge Computing.- Controlling BigGAN Image Generation with a Segmentation Network.- GANs for tabular healthcare data generation: a review on utility and privacy.- Preferences and Recommender Systems.- An Ensemble Hypergraph Learning framework for Recommendation.- KATRec: Knowledge Aware aTtentive Sequential Recommendations.- Representation Learning and Feature Selection.- Elliptical Ordinal Embedding.- Unsupervised Feature Ranking via Attribute Networks.- Responsible Artificial Intelligence.- Deriving a Single Interpretable Model by Merging Tree-based Classifiers.- Ensemble of Counterfactual Explainers. Riccardo Guidotti and Salvatore Ruggieri.- Learning Time Series Counterfactuals via Latent Space Representations.- Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion Detection Systems.- Local Interpretable Classifier Explanations with Self-generated Semantic Features.- Privacy risk assessment of individual psychometric profiles.- The Case for Latent Variable vs Deep Learning Methods in Misinformation Detection: An Application to COVID-19.- Spatial, Temporal and Spatiotemporal Data.- Local Exceptionality Detection in Time Series Using Subgroup Discovery.- Neural Additive Vector Autoregression Models for Causal Discovery in Time Series.- Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data.

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Book SynopsisThis text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they can be applied and explained within improving decisions.Table of ContentsChapter 1. Introduction to Analytics and Data Science. Chapter 2. Data Types Structure & Data Preparation Process. Chapter 3. Data Exploration and Data Visualization. Chapter 4. Evaluating Predictive Performance. Chapter 5. Decision Trees & Ensemble. Chapter 6. Regression Models. Chapter 7. Neural Networks. Chapter 8. Model Deployment.

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Book SynopsisThis textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author’s approach enables strategic co-training of output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications.Table of ContentsIntroduction1. Network Architectures2. Learning Processes3. Recurrent Neural Networks (RNN)4. Gated RNN: The Long Short-Term Memory (LSTM) RNN5. Gated RNN: The Gated Recurrent Unit (GRU) RNN6. Gated RNN: The Minimal Gated Unit (MGU) RNN

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Book SynopsisThis book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection. Table of ContentsOral Presentation.- Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification.- State Space approximation of Gaussian Processes for time-series forecasting.- Fast Channel Selection for Scalable Multivariate Time Series Classification.- Temporal phenotyping for characterisation of hospital care pathways of COVID patients.- A New Multivariate Time Series Co-clustering Non-Parametric Model Applied to Driving-Assistance Systems Validation.- TRAMESINO: Trainable Memory System for Intelligent Optimization of Road Traffic Control.- Detection of critical events in renewable energy production time series.- Poster Presentation.- Multimodal Meta-Learning for Time Series Regression.- Cluster-based Forecasting for Intermittent and Non-intermittent Time Series.- State discovery and prediction from multivariate sensor data.- RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds.- From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information.

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Book SynopsisThis book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.*The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part II named: Pattern mining; Graph mining; Text mining; Multimedia and time series data mining; and Classification, clustering and recommendation. * The conference was originally planned for December 2021, but was postponed to 2022.

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Book SynopsisPrivacy models and concepts.- Microdata protection.- Statistical table protection.- Synthetic data generation methods.- Synthetic data generation software.- Disclosure risk assessment.- Spatial and georeferenced data.- Machine learning and privacy.- Case studies.

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Book Synopsis.- Smart Helmet..- Brain Tumor Detection Using CNN..- An Efficient Machine Learning Enabled Algorithm to Predict Student Performance in Higher  Education..- Accelerating Neural Network Model Deployment  with Transfer Learning Techniques using Cloud Edge Smart IoT Architecture..- Machine Learning Revolutionizing in Gestational Diabetes Care..- Detection of Malwares on Android Devices Using A Genetic Algorithm Based Feature Selection And Machine Learning..- Deep Learning Based Health Care System Using Chest X Ray Scans for Image Classification..- Advancements and Challenges in Text Summarization: An Overview of Methods and Strategies in Brief..- A Novel Methodology to Predict and Detect the Consumption of Power for Smart Commercial Areas using Stacked GRU and LSTM (called Deep GRULS Architecture).

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Book Synopsis.- Quantitative Estimation of Reputation Risk..- Dissecting Data Practices in Android Apps: A Comparative Study of Data Collection and Sharing Behaviors..- Model-Based Meta-Reinforcement Learning for Hyperparameter Optimization..- Towards Sustainable Precision: Machine Learning for Laser Micromachining Optimization..- Association Rules Mining with Auto-Encoders..- Using Contrastive Learning to Map Stylistic Similarities in Narrative Writers..- Automatic classification of signal and noise in functional magnetic resonance imaging scans using convolutional neural networks..- How Resilient are Language Models to Text Perturbations?..- Emotional Sequential Influence Modeling on False Information..- CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care Data Interoperability..- Padel two-dimensional tracking extraction from monocular video recordings..- Drowsiness Detection Using Vital Sign Sensors and Deep Learning on Smartwatches..- Benchmarking out of the box Open-Source LLMs for Malware Detection based on API Calls sequences..- Multimodal Visio-lingual Content Analysis to Detect Fake Content on Reddit..- MetaLIRS: Meta-learning for Imputation and Regression Selection..- Pipeline for Semantic Segmentation of Large Railway Point Clouds..- Preliminary Investigation on Machine Learning and Deep Learning Models for Change of Direction Classification in Running..- Efficient Radar Scheduling Using Genetic Algorithms and Stochastic Heuristic Initialization..- Towards a Communication Specification Language for Heterogeneous Service Orchestration based on Process Calculus and Holonic Multi-agent Systems..- Counterfactual Explanations for Sustainable Tourism Indicators..- Tracking Healthy Organs in Medical Scans to Improve Cancer Treatment by Using UW-Madison GI Tract Image Segmentation..- Low consumption models for disease diagnosis in isolated farms..- Fast and Scalable Recommendation Retrieval Model with Mixed Attention and Knowledge Distillation..- Federated Learning for Vietnamese SMS Spam Detection using Pre-Trained PhoBERT..- Deep Learning Inference on Edge: A Preliminary Device Comparison..- Causal Explanation of Graph Neural Networks..- The contribution of social sciences driven user studies to the development of human-centered artificial intelligence..- Towards Reliable Drift Detection and Explanation in Text Data..- Using Diffusion Models for Data Augmentation on Limited Rodent OCT Datasets..- Employing Explainable AI techniques for Air Pollution: An ante-hoc and post-hoc approach in dioxide nitrogen forecasting..- Predicting employee attrition in a multi-company setting..- A deep-learning approach for the identification of new subtypes of lung cancer ..- Loss Function Role in Processing Sequences with Heavy-Tailed Distributions..- Cooperative-Competitive Decision-Making in Resource Management: A Reinforcement Learning Perspective..- Improving Speech Emotion Recognition: Novel Aggregation Strategies for Self-Supervised Features..- Refining Multiple Instance Learning with Attention Regularization for Whole Slide Image Classification..- Evaluating performance and trustworthiness of RAG systems for generating administrative text..- Blueprint of Tomorrow: Contrasting Off-line and On-line Drawing Tasks for Alzheimer's Disease Screening..- Digital Mental Health Apps: Key Features and User Engagement for Better Wellness..- Automatic PDF Document Classification with Machine Learning..- Contributions on Mixtures of Polynomials for Hybrid Bayesian Networks..- Age-unbiased Facial Emotion Recognition with Regularizing Self-attention Value Vector..- Assessing the Impact of Temporal Data Aggregation on the Reliability of Predictive Machine Learning Models..- Topic modeling in Telegram channels during the Russia-Ukraine conflict..- Structural and Semantic Data Layers in Time Series Analyses.

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Book Synopsis.- A Divide-and-Conquer Approach for Container License Plate Detection Using Multi-Frame Analysis..- Smart Sign Language Decoder..- Hotel's Price Prediction Based on Country Specific Data..- New Approach to Support the Breast Cancer Diagnosis Process Using Frequent Pattern Growth and Stacking Based on Machine Learning Techniques..- An Ontology-Lexicon-Driven Approach for Refining Sentiment Analysis Processes..- Characterising Class Imbalance in Transportation Mode Detection: An Experimental Study..- LeakG3PD: a Python generator and simulated Water Distribution System dataset..- Providing Informative Feedback in a Low-Cost Rehabilitation System using Machine Learning..- Noise tolerance and robustness ranking in Machine Learning models..- A supervised clustering approach to detect similar soccer players..- Three-Part Genetic Algorithm to Optimize the Outbound Train Loading Process Using a Multiple Travelling Salesman Problem Approach..- Using Data Augmentation For Improving Text Summarization..- Special Session on Predictive and Prescriptive Models for Smart Cities' Applications..- Sustainable demand-responsive transportation: A case study in rural Guimarães..- CLARA: Semi-Automatic Retraining System..- A grid-based approach for ambulance dispatch in critical emergencies within static systems.Optimizing vehicle coordination at multi-lane intersections using traffic control algorithms..- Optimizing Pedestrian Paths to Minimize Exposure to Urban Pollution Through Traffic Data Analysis..- Optimizing UCO Container Placement in Urb. Envs: A GA Approach..- Special Session on Computational intelligence on Renewable Energies and Sustainable Automation..- Data analysis and anomaly detection in a wind farm with k-Nearest Neighbors..- Development of a Database for Convolutional Neural Networks Simulating CFD Analysis..- Special Session on Example-based Explainable Artificial Intelligence.Entity Examples for Explainable Query Target Type Identification with LLMs..Near Hit and Near Miss Example Explanations for Model Revision in Binary Image Classification.- Special Session on Explainability and Fairness in Decision Support..- Evaluative Customized Naïve Associative Classifier: promoting equity in AI for the selection and promotion of human resources.Clustering of Serious Game Traces using Formal Concept Analysis.LORE4GroupRS: Explaining group recommendations supported by a local rule-based approach..- Special Session on Federated Learning, Intelligent Fusion and Applications (FLIFA).- Comparing MAE and RMSE as fitness of Genetic Algorithm for optimizing Echo State Network hyperparameters with different probabilistic distributions..- Federated Learning with Discriminative Naive Bayes Classifier..- Advances in Home Care and Real-Time Vital Signs Monitoring..- Exploring Data Symbion EI deep learning and model sharing modules..- A New Dataset for Analyzing Battery Failures in Wheelchairs..- A Methodology for Automated Conversion of Axis-Aligned to Polygonal and Oriented Bounding Box Annotations in Aerial Imagery Object Detection..- Multi-Layered Asynchronous Consensus-based Federated Learning (MACoL)..- .- Comparative study of Federated Learning algorithms based on SPADE agents..- Robotic Precision Fitness: Accurate Pose Training for Elderly Rehabilitation..- Special Session on Quantum Computing for Machine Learning and Optimization (Q4ML-Opt)..- Hybrid Quantum Solvers in Production: how to succeed in the NISQ era?..- QUBO Optimization of Electrical Grid Topologies..-Special Session on Anomaly Detection with Machine Learning..- Indecision-aware Deep Active Anomaly Detection..- Special Session on Developing AI Curricula for Pre-University Education..- Educational management as an ensure of high-quality standards, focused on the added value of a public university..- Identification of Areas for Improvement in Digital Pedagogical Competencies through Information Technologies, Communication, and Artificial Intelligence: An Innovative Approach in Teacher Training..- What Students Should Learn and Teachers Must Know about Artificial Intelligence..- Simplification of Image Segmentation and Object Detection Teaching Materials..- Educational Computer Vision Materials for Classification and Tracking of Objects..- Starting point in the introduction of AI in VET: Analysis and proposals in Spain..- Advancing Robotics Education: Integrating Large Language Models for Natural Language .- Programming in VET..- A Comprehensive Digital Solution for Identifying and Addressing Academic Risk in .- Middle Education..- Special Session on Data Selection in Machine Learning (6th Edition)..- Data Mining In Credit Card Approval: Feature Importance as a Comparison..- Special Session on Computational Intelligence for Imbalanced Classification..- 2D Convolutional Neural Networks for Alzheimer's Disease Classification from Brain MRI.

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