Data mining Books

Book Synopsis.- Applications of AI/ML in KDM, Cloud Computing & Security..- Healthify App Using Blockchain with Cloud..- A Systematic Review of Various Deep Learning Techniques for Network Intrusion Detection System..- Intrusion Detection System Trends: An Overview of Current Advances in IoV & Communication Networks..- Automation Xtreme -A web automation AI Tool..- Defending the Digital Frontier URL-based Phishing Detection Extension..- Guarding the Digital Frontier: A Logistic Regression Approach to Malware Detection..- Hybrid Efficient IDS Against Adversarial Attacks in IoT Networks..- Data Analytics..- Real-Time Soil Moisture Sensing using Arduino for Automated Plant Irrigation System..- Campus Placement and Salary Prediction: Leveraging Machine Learning for Enhanced Employability..- Exploring Corrosion Detection: Deep Learning and Ensemble Approaches Analysis..- Comic Generation using AI - A Review..- Resid

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Book Synopsis.- Keynote Talk..- Sparse Matrix Algorithms for Evolving Neural Networks..- Invited Talk..- Data integration in the AI era: research trends and still open issues..- Tutorial..- Leveraging machine learning techniques for customer data deduplication-hard-won lessons from a real-world project in the financial industry,.- Data mining and knowledge discovery..- FairFES - Fast Exact Sampling for Fair Classification..- Autism Detection by Analyzing Handwriting Characteristics of Chinese Characters via Deep Learning Models..- FNoDe: Faulty Node Detection in Microservices Architecture..- An Enhanced FP-Growth Algorithm with Hybrid Adaptive Support Threshold for Association Rule Mining..- Sequential data analytics and recommendation systems..- Entity Resolution for Streaming Data with Embeddings..- Cross-Modal Sequential Point-of-Interest Recommendation with Lightweight Hybrid Fusion Strategy..- Alternatives to Shallow Autoencoders for Collaborative Filtering..- Accurate Concept Drift Detection without Updating Autoencoders..- Graph data processing and analytics..- Parallel and Distributed SQL/PGQ Query Processing for Property Graphs..- Graph Constraint Language for Industrial Knowledge Graphs and Machine Learning..- SemViSG : Semantic Enrichment and Visualization of Software Graphs..- Data management and Indices..- Certainty Attacks Using Explainability Preprocessing..- Integrating Bitcoin Transactions into Relational Databases for IoT: Challenges and Solutions..- Effects of Response Length on User Search Experience in Spoken Conversational Search..- Fair Proportional Top-k Ranking..- PAID: Power-efficient AI-optimized Databases..- On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data..- A Bayesian Reinforcement Learning Framework for Online Index Tuning..- Large language models (LLMs)..- Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-wise Multiview Clustering and Large Language Models..- Parameter Drift as a Signal for Membership Inference in Overfit-Tuned LLMs..- MicroSuggest: Kernel-Aware Microservice Decomposition..- TraceTune: Targeted Fine-Tuning of Attention Heads for Text-to-SQL..- Neural networks..- ONNYX : Optimized Neural Networks Yielding eXplainable insights from ECG signals-based data streams..- SpaPool: Soft Partition Assignment Pooling for Graph Neural Networks..- Prediction of iterative solvers' convergence using pretraining by natural images..- Local-aware Convolutional Modulation for Short-Term Sequential Recommendation.

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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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Book SynopsisThe aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.

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Book SynopsisThe immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos. This edited book gathers together an international set of researchers to investigate the possibilities offered by data-fabric solutions; the volume focuses in particular on data architectures and on semantic changes in future data landscapes.

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Book SynopsisSocial network analysis increasingly bridges the discovery of patterns in diverse areas of study as more data becomes available and complex. Yet the construction of huge networks from large data often requires entirely different approaches for analysis including; graph theory, statistics, machine learning and data mining. This work covers frontier studies on social network analysis and mining from different perspectives such as social network sites, financial data, e-mails, forums, academic research funds, XML technology, blog content, community detection and clique finding, prediction of user’s- behavior, privacy in social network analysis, mobility from spatio-temporal point of view, agent technology and political parties in parliament. These topics will be of interest to researchers and practitioners from different disciplines including, but not limited to, social sciences and engineering.Table of ContentsA Randomized Approach for Structural and Message based Private Friend Recommendation in Online Social Networks; B. K. Samanthula, W.Jiang.- Context Based Semantic Relations in Tweets; O. Ozdikis et al.- Fast exact and approximate computation of betweenness centrality in social networks; M. Baglioni et al.- Network Simulation; E. Franchi.- Early Stage Conversation Catalysts on Entertainment-Based Web Forums; J. Lanagan et al.- Predicting Users Behaviours in Distributed Social Networks Using Community Analysis ; B. Ngonmang et al.- What should we protect? Defining differential privacy for social network analysis; C. Task, C.Clifton.- Complex Network Analysis of Research Funding: A Case Study of NSF Grants; H. Kardes et al.- Community Evolutionary Events in Online Social Networks; M. Abulaish, S. Yousuf Bhat.-@Rank: Personalized Centrality Measure for Email Communication Networks; P. Lubarski, M. Morzy.-Twitter Sentiment Analysis: How To Hedge Your Bets In The Stock Markets; T.Rao, S. Srivastava.- The Impact of Measurement Time on Subgroup Detection in Online Communities; S. Zeini et al.- Spatial and Temporal Evaluation of Network-Based Analysis of Human Mobility; M. Coscia et al.- An Ant based Particle Swarm Optimization Algorithm for Maximum Clique Problem in Social networks; M. Soleimani-pouri et al.- XEngine: An XML Search Engine for Social Groups; K.Taha.- Size, diversity and components in the network around an entrepreneur: Shaped by culture and shaping embeddedness of firm relations; M. Cheraghi, T.Schott .- Content Mining of Microblogs; M.Ö. Cingiz, B. Diri.

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Book SynopsisData Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.Trade ReviewFrom the book reviews:“This book is a comprehensive collection of data preprocessing techniques used in data mining. Any readers who practice data mining will find it beneficial … . This book is an excellent guideline in the topic of data preprocessing for data mining. It is suitable for both practitioners and researchers who would like to use datasets in their data mining projects.” (Xiannong Meng, Computing Reviews, December, 2014)Table of ContentsIntroduction.- Data Sets and Proper Statistical Analysis of Data Mining Techniques.- Data Preparation Basic Models.- Dealing with Missing Values.- Dealing with Noisy Data.- Data Reduction.- Feature Selection.- Instance Selection.- Discretization.- A Data Mining Software Package Including Data Preparation and Reduction: KEEL.

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Book SynopsisThis book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.Trade Review“Charu Aggarwal, a well-known, reputable IBM researcher, has taken the time to distill the advances in the design of recommender systems since the advent of the web … . Extensive bibliographic notes at the end of each chapter and more than 700 references in the book bibliography make this monograph an excellent resource for both practitioners and researchers. … Without a doubt, this is an excellent addition to my bookshelf!” (Fernando Berzal, Computing Reviews, February, 2017)Table of ContentsAn Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.

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Book SynopsisThis illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.Trade Review“This title presents recent research and future trends in the area of big data. … It will be of value to students and researchers looking for research topics and to data scientists exploring ongoing work in the field of big data. Summing Up: Recommended. Graduate students; faculty and professionals.” (C. Tappert, Choice, Vol. 54 (7), March, 2017)Table of ContentsPart I: Data Science Applications and Scenarios An Interoperability Framework and Distributed Platform for Fast Data ApplicationsJosé Carlos Martins Delgado Complex Event Processing Framework for Big Data ApplicationsRenta Chintala Bhargavi Agglomerative Approaches for Partitioning of Networks in Big Data ScenariosAnupam Biswas, Gourav Arora, Gaurav Tiwari, Srijan Khare, Vyankatesh Agrawal and Bhaskar Biswas Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data PerspectiveYing Xie, Jing (Selena) He and Vijay V. Raghavan Part II: Big Data Modelling and Frameworks A Unified Approach to Data Modelling and Management in Big Data EraCatalin Negru, Florin Pop, Mariana Mocanu and Valentin Cristea Interfacing Physical and Cyber Worlds: A Big Data PerspectiveZartasha Baloch, Faisal Karim Shaikh and Mukhtiar A. Unar Distributed Platforms and Cloud Services: Enabling Machine Learning for Big DataDaniel Pop, Gabriel Iuhasz and Dana Petcu An Analytics Driven Approach to Identify Duplicate Bug Records in Large Data RepositoriesAnjaneyulu Pasala, Sarbendu Guha, Gopichand Agnihotram, Satya Prateek B and Srinivas Padmanabhuni Part III: Big Data Tools and Analytics Large Scale Data Analytics Tools: Apache Hive, Pig and HBaseN. Maheswari and M. Sivagami Big Data Analytics: Enabling Technologies and ToolsMohanavadivu Periasamy and Pethuru Raj A Framework for Data Mining and Knowledge Discovery in Cloud ComputingDerya Birant and Pelin Yıldırım Feature Selection for Adaptive Decision Making in Big Data AnalyticsJaya Sil and Asit Kumar Das Social Impact and Social Media Analysis Relating to Big DataNirmala Dorasamy and Nataša Pomazalová

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Book SynopsisThis textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology"This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at ChicagoTrade Review“I can strongly recommend this book to any graduate students who want to learn the theoretical parts of the broad area of data mining. It offers enough material for several semesters of data mining or machine learning courses. Researchers and practitioners who want to survey the principles and concepts of current data mining topics and learn their theoretical perspective would benefit greatly from this book.” (Daijin Ko, Mathematical Reviews, May, 2017)“Written by one of the most prodigious editors and authors in the data mining community, Data mining: the textbook is a comprehensive introduction to the fundamentals and applications of data mining. The recent drive in industry and academic toward data science and more specifically “big data” makes any well-written book on this topic a welcome addition to the bookshelves of experienced and aspiring data scientists… The writing style is excellent and the author managed to provide sufficient mathematical background in terms of formal proofs and notations, in order to make it self-contained and scientifically appealing to more theory-oriented readers.Covering more than 20 chapters and 700 pages, Aggarwal provides a unique textbook and reference to data mining, which I recommend to every reader working on or learning about data mining.” (Radu State, ACM Computing Reviews #CR143869)Table of ContentsIntroduction to Data Mining.- Data Preparation.- Similarity and Distances.- Association Pattern Mining.- Association Pattern Mining: Advanced Concepts.- Cluster Analysis.- Cluster Analysis: Advanced Concepts.- Outlier Analysis.- Outlier Analysis: Advanced Concepts.- Data Classification.- Data Classification: Advanced Concepts.- Mining Data Streams.- Mining Text Data.- Mining Time-Series Data.- Mining Discrete Sequences.- Mining Spatial Data.- Mining Graph Data.- Mining Web Data.- Social Network Analysis.- Privacy-Preserving Data Mining.

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Book SynopsisThis textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.Trade Review“This 430-page book contains an excellent collection of information on the subject of practical algorithms used in data science. The discussion of each algorithm starts with some basic concepts, followed by a tutorial with real datasets and detailed code examples in Python or R. Each chapter has a set of exercise problems so readers can practice the concepts learned in the chapter. … a good reference for practitioners, or a good textbook for graduate or upper-class undergraduate students.” (Xiannong Meng, Computing Reviews, September, 2017)“This textbook on practical data analytics unites fundamental principles, algorithms, and data. … this book is devoted to upper-division undergraduate and graduate students in mathematics, statistics, and computer science. It is intended for a one- or two-semester course in data analytics and reflects the authors’ research experience in data science concepts and the teaching skills in various areas. … The text is eminently suitable for self-study and an exceptional resource for practitioners.” (Krzysztof J. Szajowski, zbMATH 1367.62005, 2017) Table of ContentsIntroduction.- Data Mapping and Data Dictionaries.- Scalable Algorithms and Associative Statistics.- Hadoop and MapReduce.- Data Visualization.- Linear Regression Methods.- Healthcare Analytics.- Cluster Analysis.- k-Nearest Neighbor Prediction Functions.- The Multinomial Naive Bayes Prediction Function.- Forecasting.- Real-time Analytics.

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Book SynopsisThe book offers a timely snapshot of neural network technologies as a significant component of big data analytics platforms. It promotes new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on different computing platforms (e.g. neuromorphic, graphics processing units (GPUs), clouds, clusters); and big data analytics applications to solve real-world problems (e.g. weather prediction, transportation, energy management). The book, which reports on the second edition of the INNS Conference on Big Data, held on October 23–25, 2016, in Thessaloniki, Greece, depicts an interesting collaborative adventure of neural networks with big data and other learning technologies.Table of ContentsPredicting human behavior based on web search activity: Greek referendum of 2015.- Compact Video Description and Representation for Automated Summarization of Human Activities.- Attribute Learning for Network Intrusion Detection.- A Fast Deep Convolutional Neural Network for face detection in Big Visual Data.- Learning Symbols by Neural Network.- Designing HMMs models in the age of Big Data.- Extended Formulations for Online Action Selection on Big Action Sets.- Multi-Task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports.- An infrastructure and approach for infering knowledge over Big Data in the Vehicle Insurance Industry.- Unified Retrieval Model of Big Data.- Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species.

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Book SynopsisMachine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.Table of ContentsMachine Learning for Health Informatics.- Bagging Soft Decision Trees.- Grammars for Discrete Dynamics.- Empowering Bridging Term Discovery for Cross-domain Literature Mining in the TextFlows Platform.- Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice.- Deep learning trends for focal brain pathology segmentation in MRI.- Differentiation between Normal and Epileptic EEG using K-Nearest-Neighbors Technique.- Survey on Feature Extraction and Applications of Biosignals.- Argumentation for knowledge representation, conflict resolution, defeasible inference and its integration with machine learning.- Machine Learning and Data mining Methods for Managing Parkinson’s Disease.- Challenges of Medical Text and Image Processing: Machine Learning Approaches.- Visual Intelligent Decision Support Systems in the medical field: design and evaluation.

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Book SynopsisThis engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) Trade Review “The book is more than a typical manual. In fact, the author himself designates it as a textbook for an introductory course on data science. The chapters are richly equipped with exercises. The topics are always explained starting with a proper motivation and continuing with practical examples. This is perhaps the most outstanding feature of the book. It can serve as a regular textbook for an academic course. In fact, I should like to recommend it exactly for this purpose. On the other hand, it provides a wealth of material for people from industry, such as software engineers, and can serve as a manual for them to accomplish data science tasks. It should be noted that the book is not just a text, but a much more complex product, including a full set of lecture slides available online as well as a solutions wiki.” (P. Navrat, Computing Reviews, February, 23, 2018) ​Table of ContentsWhat is Data Science? Mathematical Preliminaries Data Munging Scores and Rankings Statistical Analysis Visualizing Data Mathematical Models Linear Algebra Linear and Logistic Regression Distance and Network Methods Machine Learning Big Data: Achieving Scale

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Book SynopsisIn this book the author argues that the basis of what we consider computer intelligence has algorithmic roots, and he presents this with a holistic view, showing examples and explaining approaches that encompass theoretical computer science and machine learning via engineered algorithmic solutions.Part I of the book introduces the basics. The author starts with a hands-on programming primer for solving combinatorial problems, with an emphasis on recursive solutions. The other chapters in the first part of the book explain shortest paths, sorting, deep learning, and Monte Carlo search. A key function of computational tools is processing Big Data efficiently, and the chapters in Part II of the book examine traditional graph problems such as finding cliques, colorings, independent sets, vertex covers, and hitting sets, and the subsequent chapters cover multimedia, network, image, and navigation data. The highly topical research areas detailed in Part III are machine learning, problem solving, action planning, general game playing, multiagent systems, and recommendation and configuration. Finally, in Part IV the author uses application areas such as model checking, computational biology, logistics, additive manufacturing, robot motion planning, and industrial production to explain how the techniques described may be exploited in modern settings.The book is supported with a comprehensive index and references, and it will be of value to researchers, practitioners, and students in the areas of artificial intelligence and computational intelligence.Table of ContentsPreface.- Towards a Characterization.- Part I, Basics.- 1. Programming Primer.- 2. Shortest Paths.- 3. Sorting.- 4. Deep Learning.- 5. Monte-Carlo Search.- Part II, Big Data.- 6. Graph data.- 7. Multimedia Data.- 8. Network Data.- 9. Image Data.- 10. Navigation Data.- Part III, Research Areas.- 11. Machine Learning.- 12. Problem Solving.- 13. Card Game Playing.- 14. Action Planning.- 15. General Game Playing.- 16. Multiagent Systems.- 17. Recommendation and Configuration Part IV, Applications.- 18. Adversarial Planning.- 19. Model Checking.- 20. Computational Biology.- 21. Logistics.- 22. Additive Manufacturing.- 23. Robot Motion Planning.- 24. Industrial Production.- 25. Further Application Areas. - Index and References

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Book SynopsisThis book constitutes the refereed proceedings of the 9th International Conference on Data-Driven Innovation, ICT Innovations 2017, held in Skopje, Macedonia, in September 2017. The 26 full papers presented were carefully reviewed and selected from 90 submissions. They cover the following topics: big data analytics, cloud computing, data mining, digital signal processing, e-health, embedded systems, emerging mobile technologies, multimedia, Internet of Things (IoT), machine learning, software engineering, security and cryptography, coding theory, wearable technologies, wireless communication, and sensor networks.Table of ContentsData-driven innovations, organized around topics such as increasing migration of socio-economic activities to the Internet.- The decline in the cost of data collection, storage and processing.- The generation and use of huge volumes of data.- Large datasets becoming a core asset in research and economy fostering new discoveries, new industries, new processes.

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Book SynopsisThis accessible and engaging textbook/guide provides a concise introduction to data structures and associated algorithms. Emphasis is placed on the fundamentals of data structures, enabling the reader to quickly learn the key concepts, and providing a strong foundation for later studies of more complex topics. The coverage includes discussions on stacks, queues, lists, (using both arrays and links), sorting, and elementary binary trees, heaps, and hashing. This content is also a natural continuation from the material provided in the separate Springer title Guide to Java by the same authors.Topics and features: reviews the preliminary concepts, and introduces stacks and queues using arrays, along with a discussion of array-based lists; examines linked lists, the implementation of stacks and queues using references, binary trees, a range of varied sorting techniques, heaps, and hashing; presents both primitive and generic data types in each chapter, and makes use of contour diagrams to illustrate object-oriented concepts; includes chapter summaries, and asks the reader questions to help them interact with the material; contains numerous examples and illustrations, and one or more complete program in every chapter; provides exercises at the end of each chapter, as well as solutions to selected exercises, and a glossary of important terms.This clearly-written work is an ideal classroom text for a second semester course in programming using the Java programming language, in preparation for a subsequent advanced course in data structures and algorithms. The book is also eminently suitable as a self-study guide in either academe or industry.Trade Review“This text is intended to provide undergraduates using Java with a concise, focused, and relatively simple coverage of some of the basic data structures in use. These include arrays, linked lists, trees, heaps (in arrays), and hash tables. … The book covers the algorithms and data structures well with clear language, abundant diagrams, and good exercises. It could be a good introduction for curricula using Java as a primary teaching language.” (Jeffrey Putnam, Computing Reviews, July, 2018)​Table of ContentsPreliminary Concepts Stacks Using Arrays Queues Using Arrays Lists Using Arrays Lists Using Objects and References Ordered Linked Lists Stacks and Queues Using References Binary Trees Sorting Heaps Hashing

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Book SynopsisThis edited volume presents advances in modeling and computational analysis techniques related to networks and online communities. It contains the best papers of notable scientists from the 4th European Network Intelligence Conference (ENIC 2017) that have been peer reviewed and expanded into the present format. The aim of this text is to share knowledge and experience as well as to present recent advances in the field. The book is a nice mix of basic research topics such as data-based centrality measures along with intriguing applied topics, for example, interaction decay patterns in online social communities. This book will appeal to students, professors, and researchers working in the fields of data science, computational social science, and social network analysis. Table of ContentsData-based centrality measures.- Extracting the Main Path of historic events from Wikipedia.- Simulating trade in economic networks with TrEcSim.- Community Aliveness: Discovering interaction decay patterns in online social communities.- Network Patterns of Direct and Indirect Reciprocity in edX MOOC Forums.- Targeting influential nodes for recovery in bootstrap percolation on hyperbolic networks.- Trump versus Clinton – Twitter communication during the US primaries.- Extended feature-driven graph model for Social Media Networks.- Market basket analysis using minimum spanning trees.- Behavior-based relevance estimation for social networks interaction relations.- Sponge walker: Community detection in large directed social networks using local structures and random walks.- Identifying promising research topics in Computer Science.- Identifying accelerators of information diffusion across social media channels .- Towards an ILP approach for learning privacy heuristics from users' regrets.- Strength of nations: A case study on estimating the influence of leading countries using social media analysis.- Incremental learning in dynamic networks for node classification.

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Book SynopsisThis book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.Table of Contents1 The Data Science Era.- 2 What is Data Science.- 3 Data Science Thinking.- 4 Data Science Challenges.- 5 Data Science Discipline.- 6 Data Science Foundations.- 7 Data Science Techniques.- 8 Data Economy and Industrialization.- 9 Data Science Applications.- 10 Data Profession.- 11 Data Science Education.- 12 Prospects and Opportunities in Data Science.

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Book SynopsisSie haben manchmal den Eindruck, Sie ertrinken in Daten? Kennen Sie schon die großartigen Datenanalysewerkzeuge von Excel? Stephen L. Nelson und Elizabeth C. Nelson zeigen Ihnen, wie Sie zu Ihren Daten PivotTables und PivotCharts erstellen, welche Excel-Funktionen zu Statistik und Finanzwesen es gibt und wie Sie Excel mit Daten aus externen Datenbanken nutzen. Erfahren Sie endlich, was all die vermeintlich todlangweiligen Zahlen wirklich zu bedeuten haben. Mit diesem Buch können Sie die Verarbeitung der Daten Excel überlassen und Ihre Zeit wieder für echte Einsichten und Entscheidungen nutzen.Table of ContentsÜber den Autor 7 Einführung 21 Über dieses Buch 21 Was Sie problemlos ignorieren können 21 Was Sie nicht ignorieren sollten (es sei denn, Sie sind ein Masochist) 22 Törichte Annahmen über den Leser 22 Wie dieses Buch aufgebaut ist 23 Icons in diesem Buch 24 Beispieldateien zum Buch 24 Wie es weitergeht 24 Teil I Erste Schritte bei der Datenanalyse 25 Kapitel 1 Grundlagen von Excel-Tabellen 27 Was ist eine Tabelle und was hat sie mit mir zu tun? 27 Tabellen erstellen 30 Export aus einer Datenbank 30 Eine Tabelle auf die harte Tour erstellen 30 Eine Tabelle auf die weniger harte Tour erstellen 30 Daten in Tabellen analysieren 34 Einfache statistische Auswertungen 34 Tabellendaten sortieren 36 Eine Tabelle mit einem AutoFilter filtern 38 Filter entfernen 41 Den AutoFilter deaktivieren 41 Einen benutzerdefinierten AutoFilter verwenden 41 Eine gefilterte Tabelle filtern 44 Spezialfilter verwenden 44 Kapitel 2 Daten aus externen Quellen abrufen 49 Die Daten über die Variante Export/Import erhalten 49 Exportieren: Der erste Schritt 49 Importieren: Der zweite Schritt (falls erforderlich) 55 Externe Datenbanken und Tabellen auf Webseiten abrufen 64 Eine Webabfrage verwenden 64 Eine Datenbanktabelle importieren 67 Eine externe Datenbank abfragen 69 Die rohen Daten kochen 76 Kapitel 3 Daten säubern 77 Ihre importierte Arbeitsmappe bearbeiten 77 Unnötige Spalten löschen 77 Unnötige Zeilen löschen 78 Spaltenbreite anpassen 78 Zeilenhöhe anpassen 81 Nicht benötigte Zellinhalte löschen 81 Numerische Werte formatieren 82 Arbeitsblattdaten kopieren 83 Arbeitsblattdaten verschieben 83 Daten in Feldern ersetzen 83 Daten mit den Textfunktionen aufräumen 84 Na, und? 84 Die Antwort auf einige Ihrer Probleme 86 Die Funktion ERSETZEN 86 Die Funktion FEST 87 Die Funktion FINDEN 87 Die Funktion GLÄTTEN 88 Die Funktion GROSS 88 Die Funktion GROSS2 88 Die Funktion IDENTISCH 89 Die Funktion KLEIN 89 Die Funktion LÄNGE 89 Die Funktion LINKS 90 Die Funktion RECHTS 90 Die Funktion SÄUBERN 90 Die Funktion SUCHEN 91 Die Funktion T 91 Die Funktion TEIL 91 Die Funktion TEXT 92 Die Funktion VERKETTEN 92 Die Funktion WECHSELN 93 Die Funktion WERT 94 Die Funktion WIEDERHOLEN 94 Die Ergebnisse der Textfunktionen in Text konvertieren 94 Mit Gültigkeitsprüfungen für korrekte Daten sorgen 95 Teil II PivotTables und PivotCharts 99 Kapitel 4 PivotTables verwenden 101 Aus unterschiedlichen Perspektiven einen Blick auf die Daten werfen 101 Vorbereitungen für das Pivotieren 102 Den PivotTable-Assistenten verwenden 103 Mit der PivotTable herumspielen 109 Pivotieren und erneut pivotieren 109 PivotTable-Daten filtern 110 Einen Datenschnitt oder eine Zeitachse verwenden 112 PivotTable-Daten aktualisieren 113 PivotTable-Daten sortieren 114 Pseudosortierung 116 Datenelemente gruppieren und deren Gruppierung aufheben 117 Dies auswählen, jenes auswählen 119 Wo kommen die Zahlen dieser Zelle her? 119 Die Wertfeldeinstellungen festlegen 120 Festlegen, wie PivotTables aussehen und funktionieren 122 PivotTable-Optionen konfigurieren 122 Kapitel 5 PivotTable-Formeln erstellen 133 Eine weitere Standardberechnung hinzufügen 133 Berechnungsoptionen erstellen 137 Berechnete Felder und Elemente verwenden 142 Ein berechnetes Feld einfügen 142 Ein berechnetes Element hinzufügen 145 Berechnete Felder und Elemente entfernen 148 Die Formeln für berechnete Felder und Elemente überprüfen 149 Die Lösungsreihenfolge ansehen und ändern 150 Daten aus einer PivotTable abrufen 152 Alle Werte einer PivotTable abrufen 152 Einen Wert aus einer PivotTable abrufen 153 Argumente der Funktion PIVOTDATENZUORDNEN 154 Kapitel 6 PivotCharts verwenden 157 Warum sollte ich ein PivotChart verwenden? 157 Vorbereitungen für das Pivotieren 158 Den PivotChart-Assistenten einsetzen 159 Mit dem PivotChart herumspielen 164 Pivotieren und erneut pivotieren 164 PivotChart-Daten filtern 165 PivotChart-Daten aktualisieren 168 Datenelemente gruppieren und deren Gruppierung aufheben 169 Mit den Diagrammbefehlen PivotCharts erstellen 171 Kapitel 7 PivotCharts anpassen 173 Einen Diagrammtyp wählen 173 Diagrammformatvorlagen verwenden 174 Das Diagrammlayout ändern 174 Diagrammtitel und Achsentitel 174 Diagrammlegende 177 Datenbeschriftungen 178 Diagrammdatentabelle 180 Diagrammachsen 181 Diagrammgitternetzlinien 183 Ein Diagramm verschieben 183 Die Zeichnungsfläche formatieren 185 Den Diagrammbereich formatieren 186 Füllmuster für das Diagramm 186 Diagrammschriften formatieren 187 3D-Diagramme formatieren 187 Die Wände eines 3D-Diagramms formatieren 187 Den Befehl »3D-Drehung« verwenden 187 Teil III Fortgeschrittene Werkzeuge 189 Kapitel 8 Die Datenbankfunktionen verwenden 191 Schneller Überblick zu Funktionen 191 Die Syntaxregeln von Funktionen 192 Eine Funktion manuell eingeben 192 Eine Funktion mit dem Funktionsassistenten eingeben 193 Die Funktion DBMITTELWERT verwenden 198 Die Funktionen DBANZAHL und DBANZAHL2 verwenden 201 Die Funktion DBAUSZUG verwenden 203 Die Funktionen DBMAX und DBMIN verwenden 205 Die Funktion DBPRODUKT verwenden 206 Die Funktionen DBSTDABW und DBSTDABWN verwenden 207 Die Funktion DBSUMME verwenden 209 Die Funktionen DBVARIANZ und DBVARIANZEN verwenden 210 Kapitel 9 Die statistischen Funktionen verwenden 213 Elemente eines Datasets zählen 213 ANZAHL: Zellen mit Werten zählen 213 ANZAHL2: Alternative Zählweise von Zellen mit Werten 215 ANZAHLLEEREZELLEN: Leere Zellen zählen 215 ZÄHLENWENN: Zellen zählen, die Bedingungen entsprechen 215 ZÄHLENWENNS: Zellen zählen, die Bedingungen entsprechen 216 VARIATIONEN und VARIATIONEN2: Permutationen zählen 216 KOMBINATIONEN: Kombinationen zählen 217 Mittelwert, Modus und Median 217 MITTELABW: Durchschnittliche absolute Abweichung 217 MITTELWERT: Arithmetisches Mittel 218 MITTELWERTA: Alternative Berechnung des Durchschnitts 218 MITTELWERTWENN und MITTELWERTWENNS: Wählerische Durchschnitte 219 GESTUTZTMITTEL: Den Mittelwert stutzen 220 MEDIAN: Den Median berechnen 221 MODUS: Den Modalwert berechnen 221 GEOMITTEL: Geometrisches Mittel 222 HARMITTEL: Harmonisches Mittel 222 Werte, Ränge und Quantile 222 MAX: Der größte Wert 223 MAX2: Alternative Berechnung des größten Werts 223 MIN: Der kleinste Wert 223 MIN2: Alternative Berechnung des kleinsten Werts 223 KGRÖSSTE: Den k-größten Wert ermitteln 224 KKLEINSTE: Den k-kleinsten Wert ermitteln 224 RANG, RANG.MITTELW und RANG.GLEICH: Den Rang eines Arraywerts bestimmen 224 QUANTILSRANG.EXKL und QUANTILSRANG.INKL: Den Quantilsrang bestimmen 226 QUANTIL.EXKL und QUANTIL.INKL: Einen bestimmten Quantilsrang finden 226 QUARTILE.EXKL und QUARTILE.INKL: Einen bestimmten Quartilsrang finden 227 HÄUFIGKEIT: Häufigkeitsverteilung ermitteln 228 WAHRSCHBEREICH: Wahrscheinlichkeit von Werten 230 Standardabweichungen und Varianzen 231 STABW.S: Standardabweichung einer Stichprobe 231 STABWA: Alternative Berechnung der Standardabweichung einer Stichprobe 232 STABW.N: Standardabweichung einer Grundgesamtheit 232 STABWNA: Alternative Berechnung der Standardabweichung einer Grundgesamtheit 233 VAR.S: Varianz einer Stichprobe 233 VARIANZA: Alternative Varianz 234 VAR.P: Varianz einer Grundgesamtheit 234 VARIANZENA: Alternative Berechnung der Varianz einer Grundgesamtheit 234 KOVARIANZ.P und KOVARIANZ.S: Kovarianzen 234 SUMQUADABW: Summe der quadrierten Abweichungen 235 Normalverteilungen 235 NORM.VERT: Wahrscheinlichkeit, dass X auf oder unter einen bestimmten Wert fällt 235 NORM.INV: X, das einer angegebenen Wahrscheinlichkeit entspricht 236 NORM.S.VERT: Wahrscheinlichkeit, dass eine Variable innerhalb von z-Standardabweichungen liegt 237 NORM.S.INV: Quantil ermitteln, das einer vorgegebenen Wahrscheinlichkeit entspricht 237 STANDARDISIERUNG: Z-Wert für einen angegebenen Wert 238 KONFIDENZ: Konfidenzintervall für den Mittelwert einer Grundgesamtheit 238 KURT: Kurtosis 239 SCHIEFE und SCHIEFE.P: Schiefe einer Verteilung 240 GAUSS: Wahrscheinlichkeit, dass ein Wert in einen Bereich fällt 240 PHI: Dichtefunktion einer Standardnormalverteilung 240 t-Verteilungen 241 T.VERT: Linksseitige Student-t-Verteilung 241 T.VERT.RE: Rechtsseitige t-Verteilung 241 T.VERT.2S: t-Verteilung für zwei Endflächen 242 T.INV: Linksseitiges Quantil der t-Verteilung 242 T.INV.2S: Zweiseitiges Quantil der t-Verteilung 242 T.TEST: Wahrscheinlichkeit, dass zwei Stichproben aus Grundgesamtheiten mit demselben Mittelwert stammen 243 F-Verteilungen 243 F.VERT: Wahrscheinlichkeit der linksseitigen F-Verteilung 243 F.VERT.RE: Wahrscheinlichkeit der rechtsseitigen F-Verteilung 244 F.INV: Linksseitiger F-Wert für die Wahrscheinlichkeit einer F-Verteilung 244 F.INV.RE: Rechtsseitiger F-Wert für die Wahrscheinlichkeit einer F-Verteilung 244 F.TEST: Wahrscheinlichkeit, dass sich die Varianzen signifikant unterscheiden 245 Binomialverteilungen 245 BINOM.VERT: Wahrscheinlichkeit bei Binomialverteilungen 245 BINOM.INV: Wahrscheinlichkeit bei Binomialverteilungen 246 BINOM.VERT.BEREICH: Binomialwahrscheinlichkeit eines Versuchsergebnisses 246 NEGBINOM.VERT: Negative Binomialverteilung 247 KRITBINOM: Kumulierte Wahrscheinlichkeiten der Binomialverteilung 248 HYPGEOM.VERT: Hypergeometrische Verteilung 248 Chi-Quadrat-Verteilungen 249 CHIQU.VERT.RE: Chi-Quadrat-Verteilung 249 CHIQU.VERT: Chi-Quadrat-Verteilung 250 CHIQU.INV.RE: Perzentile der rechtsseitigen Chi-Quadrat-Verteilung 251 CHIQU.INV: Perzentile der linksseitigen Chi-Quadrat-Verteilung 251 CHIQU.TEST: Chi-Quadrat-Test 251 Regressionsanalyse 252 PROGNOSE.LINEAR: Abhängige Variable unter Verwendung der Ausgleichsgeraden schätzen 252 PROGNOSE.ETS: Zukünftige Werte mit einer exponentiellen Glättungsmethode schätzen 253 ACHSENABSCHNITT: Gerade schneidet die y-Achse 254 RGP 255 STEIGUNG: Steigung einer Regressionsgeraden 255 STFEHLERYX: Standardfehler 255 TREND: Linearer Trend 255 RKP: Exponentielle Regression 256 VARIATION: Exponentielle Variation 256 Korrelation 256 KORREL: Korrelationskoeffizient 256 PEARSON: Pearson-Korrelationskoeffizient 257 BESTIMMTHEITSMASS: Quadrat des Pearsonschen Korrelationskoeffizienten 257 FISHER 257 FISHERINV 257 Einige ziemlich ausgefallene Wahrscheinlichkeitsverteilungen 258 BETA.VERT: Kumulierte Verteilungsfunktion der Betaverteilung 258 BETA.INV: Quantil der Betaverteilung einer Zufallsvariablen 258 EXPON.VERT: Wahrscheinlichkeiten für exponentialverteilte Zufallsvariable 259 GAMMA: Wert der Gammafunktion 259 GAMMA.VERT: Wahrscheinlichkeiten für gammaverteilte Zufallsvariable 260 GAMMA.INV: × für eine angegebene Wahrscheinlichkeit bei einer Gammaverteilung 260 GAMMALN und GAMMALN.GENAU: Natürlicher Logarithmus der Gammafunktion 260 LOGNORM.VERT: Wahrscheinlichkeiten für lognormalverteilte Zufallsvariablen 261 LOGNORM.INV: Quantile der Lognormalverteilung 261 POISSON.VERT: Wahrscheinlichkeiten einer Poissonverteilung 261 WEIBULL.VERT: Weibullverteilung 262 G.TEST: Wahrscheinlichkeit für einen Gauß-Test 262 Kapitel 10 Deskriptive Statistik 263 Populationskenngrößen ermitteln 264 Histogramm erstellen 268 Rang und Quantilsrang berechnen 271 Gleitenden Durchschnitt berechnen 274 Exponentielles Glätten 276 Zufallszahlen erzeugen 279 Stichproben ziehen 280 Kapitel 11 Mathematische Statistik 285 Das Datenanalysewerkzeug Zweistichproben t-Test verwenden 286 Das Datenanalysewerkzeug Gauß-Test verwenden 289 Ein Streudiagramm (Punktdiagramm) erstellen 291 Das Analysewerkzeug Regression verwenden 295 Das Analysewerkzeug Korrelation verwenden 298 Das Analysewerkzeug Kovarianz verwenden 300 Die ANOVA-Analysetools verwenden 301 Einen Zwei-Stichproben F-Test durchführen 303 Die Fourieranalyse verwenden 303 Kapitel 12 Modelloptimierung mit Solver 305 Modelloptimierung verstehen 306 Optimieren Sie Ihren Gewinn 306 Nebenbedingungen erkennen 306 Ein Solver-Arbeitsblatt einrichten 307 Ein Modelloptimierungsproblem lösen 311 Die Solver-Berichte durchsehen 316 Der Antwortbericht 316 Der Sensitivitätsbericht 318 Der Grenzwertbericht 319 Weitere Hinweise zu den Solver-Berichten 320 Die Solver-Optionen 321 Die Registerkarte »Alle Methoden« verwenden 322 Die Registerkarte »GRG-Nichtlinear« verwenden 323 Die Registerkarte »EA (Evolutionärer Algorithmus)« verwenden 325 Modellinformationen speichern und wiederverwenden 326 Die Solver-Fehlermeldungen verstehen 327 Solver hat eine Lösung gefunden 327 Solver hat die aktuelle Lösung durch Konvergieren erreicht 328 Solver kann die aktuelle Lösung nicht verbessern 328 Solver wurde beim Erreichen der Höchstzeit abgebrochen 328 Solver wurde auf Anforderung des Benutzers abgebrochen 328 Solver wurde beim Erreichen der Iterationsgrenze abgebrochen 328 Die Werte der Zielzelle konvergieren nicht 329 Solver konnte keine realisierbare Lösung finden 329 Die für diesen LP Solver erforderlichen Linearitätsbedingungen sind nicht erfüllt 329 Das Problem ist für die Verarbeitung durch Solver zu groß 329 Fehlerwert bei Solver in der Zielzelle oder einer Nebenbedingungszelle 330 Es ist nicht genügend Arbeitsspeicher zur Lösung des Problems verfügbar 330 Fehler im Modell. Überprüfen Sie, ob alle Zellen und Nebenbedingungen gültig sind 330 Teil IV Der Top-Ten-Teil 331 Kapitel 13 Zehn Dinge, die Sie über Statistik wissen sollten 333 Beschreibende Statistiken sind unkompliziert 333 Durchschnitte sind nicht immer einfach 334 Standardabweichungen beschreiben die Streuung 335 Eine Beobachtung ist eine Beobachtung 336 Eine Stichprobe ist eine Untermenge von Werten 336 Induktive Statistik ist cool, aber auch kompliziert 337 Wahrscheinlichkeitsverteilungsfunktionen sind nicht immer kompliziert 338 Gleichverteilung 338 Normalverteilung 339 Parameter sind nicht so kompliziert 340 Die Schiefe und die Wölbung beschreiben die Form der Wahrscheinlichkeitsverteilung 341 Konfidenzintervalle scheinen auf den ersten Blick kompliziert zu sein, sie sind jedoch nützlich 341 Kapitel 14 Fast zehn Tipps für die Präsentation von Tabellenergebnissen und die Analyse von Daten 343 Geben Sie sich beim Import der Daten Mühe 343 Entwerfen Sie Informationssysteme so, dass sie aussagekräftige Daten produzieren 344 Vergessen Sie die externen Datenquellen nicht! 345 Addieren Sie! 345 Erkunden Sie immer die beschreibenden Statistiken 346 Achten Sie auf Trends 346 Kreuztabellierung 347 Diagramme, bitte! 347 Hüten Sie sich vor induktiver Statistik 347 Kapitel 15 Zehn Tipps zur visuellen Analyse und Präsentation von Daten 349 Finden Sie den passenden Diagrammtyp 349 Verwenden Sie die Botschaft Ihres Diagramms als Diagrammtitel 351 Seien Sie bei Kreisdiagrammen auf der Hut 352 Erwägen Sie bei kleinen Datasets den Einsatz von PivotCharts 352 Vermeiden Sie 3D-Diagramme 354 Verwenden Sie niemals 3D-Kreisdiagramme 356 Achten Sie auf falsche Datenmarkierungen 357 Verwenden Sie logarithmische Skalen 358 Vergessen Sie das Experimentieren nicht 360 Besorgen Sie sich Tufte 360 Glossar zur Datenanalyse und zu Excel 361 Stichwortverzeichnis 369

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Book SynopsisThis book constitutes the thoroughly refereed joint post-proceedings of three international workshops organized by the Japanese Society for Artificial Intelligence, held in Tokyo, Japan in June 2006 during the 20th Annual Conference JSAI 2006. The volume starts with eight award winning papers of the JSAI 2006 main conference that are presented along with the 21 revised full workshop papers, carefully reviewed and selected for inclusion in the volume.Table of ContentsAwarded Papers.- Overview of Awarded Papers – The 20th Annual Conference of JSAI.- Translational Symmetry in Subsequence Time-Series Clustering.- Visualization of Contents Archive by Contour Map Representation.- Discussion Ontology: Knowledge Discovery from Human Activities in Meetings.- Predicting Types of Protein-Protein Interactions Using a Multiple-Instance Learning Model.- Lattice for Musical Structure and Its Arithmetics.- Viewlon: Visualizing Information on Semantic Sensor Network.- Cooperative Task Achievement System Between Humans and Robots Based on Stochastic Memory Model of Spatial Environment.- People Who Create Knowledge Sharing Communities.- Logic and Engineering of Natural Language Semantics.- Logic and Engineering of Natural Language Semantics (LENLS) 3.- A Dynamic Semantics of Intentional Identity.- Prolegomena to General-Imaging-Based Probabilistic Dynamic Epistemic Logic.- Logical Dynamics of Commands and Obligations.- On Factive Islands: Pragmatic Anomaly vs. Pragmatic Infelicity.- Aspects of the Indefiniteness Effect.- Interpreting Metaphors in a New Semantic Theory of Concept.- Covert Emotive Modality Is a Monster.- Conversational Implicatures Via General Pragmatic Pressures.- Dake-wa: Exhaustifying Assertions.- Unembedded ‘Negative’ Quantifiers.- Learning with Logics and Logics for Learning.- The Fourth Workshop on Learning with Logics and Logics for Learning (LLLL2006).- Consistency Conditions for Inductive Inference of Recursive Functions.- Inferability of Closed Set Systems from Positive Data.- An Extended Branch and Bound Search Algorithm for Finding Top-N Formal Concepts of Documents.- N-Gram Analysis Based on Zero-Suppressed BDDs.- Risk Mining.- Risk Mining - Overview.- Analysis on a Relation Between Enterprise Profit and Financial State by Using Data Mining Techniques.- Unusual Condition Detection of Bearing Vibration in Hydroelectric Power Plants for Risk Management.- Structural Health Assessing by Interactive Data Mining Approach in Nuclear Power Plant.- Developing Mining-Grid Centric e-Finance Portals for Risk Management.- Knowledge Discovery from Click Stream Data and Effective Site Management.- Sampling-Based Stream Mining for Network Risk Management.- Relation Between Abductive and Inductive Types of Nursing Risk Management.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th Industrial Conference on Data Mining, ICDM 2013, held in New York, NY, in July 2013. The 22 revised full papers presented were carefully reviewed and selected from 112 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, finance and telecommunication, in medicine and agriculture, and in process control, industry and society.Table of ContentsTheoretical aspects of data mining; applications of data mining in multimedia data.- Applications of data mining in marketing and in finance.- Applications of data mining in telecommunication.- Applications of data mining in medicine and agriculture.- Applications of data mining in process control, industry and society.

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Book SynopsisMachine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.

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Book SynopsisData matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases.Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.Trade Review"The book is very well organized and exceptionally well written. Because of the depth, amount, and quality of the material that is covered, I would expect this book to be one of the standard references in future years." William E. Winkler, U.S. Bureau of the Census, Washington, DC, USATable of ContentsPart I Overview.- Introduction.- The Data Matching Process.- Part II Steps of the Data Matching Process.- Data Pre-Processing.- Indexing.- Field and Record Comparison.- Classification.- Evaluation of Matching Quality and Complexity.- Part III Further Topics.- Privacy Aspects of Data Matching.- Further Topics and Research Directions.- Data Matching Systems.

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Book SynopsisThis book constitutes the thoroughly refereed joint post-proceedings of the Third International Workshop on Mining Ubiquitous and Social Environments, MUSE 2012, held in Bristol, UK, in September 2012, and the Third International Workshop on Modeling Social Media, MSM 2012, held in Milwaukee, WI, USA, in June 2012. The 8 full papers included in the book are revised and significantly extended versions of papers submitted to the workshops. They cover a wide range of topics organized in three main themes: communities and group structure in ubiquitous social media; ubiquitous modeling and aspects of social interactions and influence.Table of ContentsHow to Carve up the World: Learning and Collaboration for Structure Recommendation.- A Topological Approach for Detecting Twitter Communities with Common Interests.- Using Geographic Cost Functions to Discover Vessel Itineraries from AIS Messages.- Social Media as a Source of Sensing to Study City Dynamics and Urban Social Behavior: Approaches, Models and Opportunities.- An Analysis of Interactions within and between Extreme Right Communities in Social Media.- Who will Interact with Whom? A Case-Study in Second Life Using Online Social Network and Location-Based Social Network Features to Predict Interactions between Users.- Identifying Influential Users by Their Postings in Social Networks.- Modeling a Web Forum Ecosystem into an Enriched Social Graph.

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Book SynopsisNeueste Errungenschaften in der Hard-und Software-Entwicklung, wie z. B. Multi-Core-CPUs und DRAM-Kapazitäten von mehreren Terabyte pro Server, förderten die Einführung einer revolutionären Technologie: das In-Memory Data Management. Diese Technologie unterstützt die flexible und extrem schnelle Analyse großer Mengen von Unternehmensdaten. Professor Hasso Plattner und seine Arbeitsgruppe am Hasso-Plattner-Institut in Potsdam, Deutschland, lehren die entsprechenden Konzepte seit Jahren und sorgen für ihre Sichtbarkeit in der Software-Industrie. Dieses Buch basiert auf dem ersten Online-Kurs der openHPI E-Learning-Plattform, die im Herbst 2012 mit mehr als 13.000 Lernenden ins Leben gerufen wurde. Das Buch richtet sich an Studierende der Informatik, speziell mit dem Schwerpunkt Software Engineering. sowie an Business-Experten, Entscheider, Software-Entwickler, IT-Experten und IT-Analysten. Themen sind - unter anderem - die physische Datenspeicherung und der Zugang, grundlegende Datenbank-Betreiber, Kompressions-Mechanismen und Algorithmen. Darüber hinaus werden Implikationen für zukünftige Enterprise-Anwendungen und deren Entwicklung diskutiert. Die Leser lernen, die radikalen Unterschiede und Vorteile der neuen Technologie gegenüber herkömmlichen zeilenorientierten und disk-basierten Datenbanken zu verstehen.Trade Review"... richtet sich folglich primär an Fachpublikum in der Praxis, bietet jedoch auch für Personen in angrenzenden Anwendungsbereichen einen fundierten Einstieg in die Materie." (in: Managementkompass In-Memory-Analytics, Heft 1, 2015)Table of ContentsDie Zukunft des Enterprise Computing.- Grundlagen der Datenbank-Speicher-Techniken.- Betreiber.- Speichertechniken.- Startschuss für eine neue Ära.

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Book SynopsisDominik Herrmann zeigt, dass die Betreiber von Nameservern, die im Internet zur Auflösung von Domainnamen in IP-Adressen verwendet werden, das Verhalten ihrer Nutzer detaillierter nachvollziehen können als bislang gedacht. Insbesondere können sie maschinelle Lernverfahren einsetzen, um einzelne Internetnutzer an ihrem charakteristischen Verhalten wiederzuerkennen und über lange Zeiträume unbemerkt zu überwachen. Etablierte Verfahren eignen sich allerdings nicht zur Anonymisierung der Namensauflösung. Daher schlägt der Autor neue Techniken zum Selbstdatenschutz vor und gibt konkrete Handlungsempfehlungen.Table of ContentsGrundlagen des Domain Name System, relevante Bedrohungen und etablierte Sicherheitsmechanismen.- Beobachtungsmöglichkeiten im Domain Name System: Rekonstruktion der besuchten Webseiten und der verwendeten Software sowie verhaltensbasierte Verkettung von Sitzungen.- Techniken zum Schutz vor Beobachtung und Verkettung mittels datenschutzfreundlicher Techniken.

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Book SynopsisDie Autoren legen beispielhafte Analysemethoden von Social-Media-Daten dar: deskriptive und Data-Mining-Methoden. Mit deren Hilfe werden kundenorientierte Geschäftsmaßnahmen eingeleitet und ein stetiges Abwägen zwischen vollautomatisierten und manuellen, kostenintensiven Reports gesteuert. Das Werk liefert eine Übersicht zu aktuell diskutierten Themen wie begleitende Emotionen, Vernetzung der interagierenden User oder Verbindung von Themen. Als Gewinn für ein Unternehmen müssen die Analysen durch eine strategische Prozedur geleitet werden, um Erkenntnisse in konkrete Handlungsempfehlungen zu überführen. Neben den Potenzialen durch die Anwendung komplexerer Analysemethoden gibt es auch konzeptionelle, technische und ethische Herausforderungen, wie die Autoren veranschaulichen.Trade Review Table of Contents

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Book SynopsisDas kompakte Fachbuch gibt einen Überblick über die Möglichkeiten von „Big Data“ im Gesundheitswesen und beschreibt anhand von ausgewählten Szenarien mögliche Einsatzgebiete.Die Autoren erläutern zentrale Systemkomponenten und IT-Standards und thematisieren anhand wichtiger Daten des Gesundheitswesens die Notwendigkeit der Strukturierung und Modellierung von Daten. Das Buch gibt Hinweise wie Geschäftsprozesse im Gesundheitswesen dokumentiert, analysiert und verbessert werden können. Anwendungsszenarien, wie die Datenanalysen für Krankenhäuser, Labore, Versicherungen und die Pharmaindustrie, zeigen die praktische Relevanz des Themas. Aber auch rechtliche und ethische Aspekte werden inhaltlich angeschnitten.Ein Buch für Entscheider in der medizinischen Leitung und Verwaltung von Krankenhäusern, Fachleute sowie niedergelassene Ärzte und Apotheker, aber auch Personen in Ausbildung und Studium im Gesundheitswesen. Table of ContentsBig-Data-Analytics im Gesundheitswesen - Medizin - Verwaltung - Forschung: Anwendungsgebiete für Big-Data-Analytics - Gesetzliche Rahmenbedingungen und Big-Data-Ethik

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Book SynopsisThis book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.Table of ContentsData Analytics - Data and Relations - Data Preprocessing - Data Visualization - Correlation - Regression - Forecasting - Classification - Clustering.

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Book SynopsisFür Leser, die bereits die Grundlagen der Wissensverarbeitung und Computernetzwerke beherrschen, gibt das Buch einen Überblick über innovative Verfahren, die die automatisierte Suche, Recherche, Klassifikation und Verwaltung von Texten im Kontext dezentraler Systeme und vor allem im WWW erlauben. Besondere Aufmerksamkeit wird dabei auf eine personalisierte Verarbeitung gerichtet, die auch zeitliche Aspekte, wie z. B. das digitale Vergessen, einbeziehen. An vielen Stellen werden auf interessante und neuartige Art und Weise Analogien aus anderen Wissensgebieten, so z. B. zur Verarbeitung von Informationen und zum Lernen im menschlichen Gehirn sowie der Natur schlechthin genutzt.Table of ContentsWissensverarbeitung im menschlichen Gehirn - Lernen - Netzwerke für die Textanalyse - Digitale Updates und digitales Vergessen - Exploration von Netzwerkstrukturen - Konzepte des Text Minings in dezentralen Systemen - Informationsmanagement im Web

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Book SynopsisThis book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications.

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Book SynopsisThis book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided.The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.Trade Review“The usage of examples and case studies enable real life application and brings asophisticated text to life. … the book is a comprehensive and thoroughly well thought out introduction to the subject of business intelligence and the reader will not be left wanting as the clear examples are numerous. … Readers interested in the value of data and the concepts behind business intelligence will find the book and its accompanying website highly informative.” (Georgette Banham, bcs, The Chartered Institute for IT, bcs.org, August, 2016)“This book focuses primarily on the data mining, data warehousing, data analytics, data visualization, data presentation, and process analysis dimensions of BI in detail. … One of the noteworthy strengths of this book is the inclusion of comprehensive lists with very recent and relevant references for BI at the end of each chapter. This should make the book very useful for academic research on the topic.” (Satya Prakash Saraswat, Computing Reviews, February, 2016)Table of Contents1 Introduction.- 2 Modeling in Business Intelligence.- 3 Data Provisioning.- 4 Data Description and Visualization.- 5 Data Mining for Cross-Sectional Data.- 6 Data Mining for Temporal Data.- 7 Process Analysis.- 8 Analysis of Multiple Business Perspectives.- 9 Summary.- A Survey on Business Intelligence Tools.

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Book SynopsisThe LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 54th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains three fully revised and extended papers and two additional extended keynotes selected from the 38th conference on Data Management - Principles, Technologies and Applications, BDA 2022. The topics cover a wide range of timely data management research topics on temporal graph management, tensor-based data mining, time-series prediction, healthcare analytics over knowledge graphs, and explanation of database query answers.Table of ContentsClock-G: Temporal graph management system.- TSPredIT: Integrated tuning of data preprocessing and time series prediction models.- A guide to the Tucker tensor decomposition for data mining: exploratory analysis, clustering and classification.- Challenges for Healthcare Data Analytics over Knowledge Graphs.- From Database Repairs to Causality in Databases and Beyond.

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Book Synopsis

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Book Synopsis

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