Data mining Books

Book SynopsisIntermediate-Advanced user levelTable of ContentsChapter 1: Getting Ready.- Chapter 2: Working with Table Design.- Chapter 3: Table Relationships and Working With Joins.- Chapter 4: Working with Calculated Data.- Chapter 5: Aggregating Data.- Chapter 6: Creating and Using Views and Friends.- Chapter 7: Working With Subqueries and Common Table Expressions.- Chapter 8: Working With Window Functions.-Chapter 9: More on Common Table Expressions.- Chapter 10: More Techniques with SQL: Triggers, Pivot Tables, and Variables.- Appendix A.

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Book SynopsisData mining refers to the activity of going through big data sets to look for relevant information. As human health care data are the most difficult of all data to collect and their primary direction is the treatment of patients, and secondarily dealing with research, almost the only vindication for collecting medical data is to benefit the disease. All data miners should take into account that Medical Knowledge Extraction is internally connected with the Evidence-Based Medical approach because it uses data for already treated or not patients and there are times that opposites to Guideline Based medical practice. Additonally all researchers should be aware when are dealing with medical databases they may face the possibility that their work will never be accepted or even used from health care professionals if all these obligations will not be correctly addressed from the early beginning. In the present book, one can find after the three introductory chapters, a number of successfully evaluated applications that have been developed after mining approaches in Big or smaller amount (according to the application) of medical Data in different fields of every day clinical practice from teams of experts. The challenging adventure of Medical Knowledge Extraction can be followed by ambitious researchers finally resulting in a successful decision support system, that some times is so novel that it will provide new directions for basic or clinical research further that the existed. At least this procedure will save the experience of the best doctors on duty and will help young residents to be better and better.Table of ContentsFor more information, please visit our website at:https://novapublishers.com/shop/medical-knowledge-extraction-from-big-data/

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Book SynopsisWith constantly expanding options such as Azure Data Lake Server (ADLS) and Azure SQL Data Warehouse (ADW), how can developers learn the process and components required to successfully move this data? Quick Start Guide to Azure Data Factory, Azure Data Lake Server, and Azure Data Warehouse teaches you the basics of moving data between Azure SQL solutions using Azure Data Factory. Discover how to build and deploy each of the components needed to integrate data in the cloud with local SQL databases. Mark Beckner's step by step instructions on how to build each component, how to test processes and debug, and how to track and audit the movement of data, will help you to build your own solutions instantly and efficiently. This book includes information on configuration, development, and administration of a fully functional solution and outlines all of the components required for moving data from a local SQL instance through to a fully functional data warehouse with facts and dimensions.

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Book SynopsisA practitioner''s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.

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Book SynopsisMaster how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialised script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: 1. An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia; 2. Options for Julia IDEs; 3. Programming structures and functions; 4. Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data pre-processing; 5. Data visualisation and some simple yet powerful statistics for data exploration purposes; 6. Dimensionality reduction and feature evaluation; 7. Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines); 8. Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.

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Book SynopsisLearn how big data and other sources of information can be transformed into valuable knowledge -- knowledge that can create incredible competitive advantage to propel a business toward market leadership. Learn through examples and experience exactly how to pick projects and build analytics teams that deliver results. Know the ethical and privacy issues, and apply the three-part litmus test of context, permission, and accuracy. Without a doubt, data and analytics are the new source of competitive advantage, but how do executives go from hype to action? Thats the objective of this book -- to assist executives in making the right investments in the right place and at the right time in order to reap the full benefits of data analytics.

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Book SynopsisMaster the concepts and strategies underlying success and progress in data science. From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientists toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework. Targeted towards data science learners of all levels, this book aims to help the reader go beyond data science techniques and obtain a more holistic and deeper understanding of what data science entails. With a focus on the problems data science tries to solve, this book challenges the reader to become a self-sufficient player in the field.

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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 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.

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

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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 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 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 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 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 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 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 SynopsisDieses Buch bietet einen Überblick über Data-Mining-Methoden, die durch Software veranschaulicht werden. Beim Wissensmanagement geht es um die Anwendung von menschlichem Wissen (Erkenntnistheorie) mit den technologischen Fortschritten unserer heutigen Gesellschaft (Computersysteme) und Big Data, sowohl bei der Datenerfassung als auch bei der Datenanalyse. Es gibt drei Arten von Analyseinstrumenten. Die deskriptive Analyse konzentriert sich auf Berichte über das, was passiert ist. Bei der prädiktiven Analyse werden statistische und/oder künstliche Intelligenz eingesetzt, um Vorhersagen treffen zu können. Dazu gehört auch die Modellierung von Klassifizierungen. Die diagnostische Analytik kann die Analyse von Sensoreingaben anwenden, um Kontrollsysteme automatisch zu steuern. Die präskriptive Analytik wendet quantitative Modelle an, um Systeme zu optimieren oder zumindest verbesserte Systeme zu identifizieren. Data Mining umfasst deskriptive und prädiktive Modellierung. Operations Research umfasst alle drei Bereiche. Dieses Buch konzentriert sich auf die deskriptive Analytik.Das Buch versucht, einfache Erklärungen und Demonstrationen einiger deskriptiver Werkzeuge zu liefern. Es bietet Beispiele für die Auswirkungen von Big Data und erweitert die Abdeckung von Assoziationsregeln und Clusteranalysen. Kapitel 1 gibt einen Überblick im Kontext des Wissensmanagements. Kapitel 2 erörtert einige grundlegende Softwareunterstützung für die Datenvisualisierung. Kapitel 3 befasst sich mit den Grundlagen der Warenkorbanalyse, und Kapitel 4 demonstriert die RFM-Modellierung, ein grundlegendes Marketing-Data-Mining-Tool. Kapitel 5 demonstriert das Assoziationsregel-Mining. Kapitel 6 befasst sich eingehender mit der Clusteranalyse. Kapitel 7 befasst sich mit der Link-Analyse. Die Modelle werden anhand geschäftsbezogener Daten demonstriert. Der Stil des Buches ist beschreibend und versucht zu erklären, wie die Methoden funktionieren, mit einigen Zitaten, aber ohne tiefgehende wissenschaftliche Referenzen. Die Datensätze und die Software wurden so ausgewählt, dass sie für jeden Leser, der über einen Computeranschluss verfügt, weithin verfügbar und zugänglich sind.Table of Contents

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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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