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Data Mining and Machine Learning

Data Mining and Machine Learning
Fundamental Concepts and Algorithms

2nd Edition

c.£57.99

textbook
  • Publication planned for: January 2020
  • availability: Not yet published - available from January 2020
  • format: Hardback
  • isbn: 9781108473989

c.£ 57.99
Hardback

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About the Authors
  • The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

    • Covers both core methods and cutting-edge research, including deep learning
    • Offers an algorithmic approach with open-source implementations
    • Short, self-contained chapters with class-tested examples and exercises allow flexibility in course design and ready reference
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    Reviews & endorsements

    'This book by Mohammed Zaki and Wagner Meira, Jr is a great option for teaching a course in data mining or data science. It covers both fundamental and advanced data mining topics, explains the mathematical foundations and the algorithms of data science, includes exercises for each chapter, and provides data, slides and other supplementary material on the companion website.' Gregory Piatetsky-Shapiro, Founder of the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD)

    'World-class experts, providing an encyclopedic coverage of all datamining topics, from basic statistics to fundamental methods (clustering, classification, frequent itemsets), to advanced methods (SVD, SVM, kernels, spectral graph theory, deep learning). For each concept, the book thoughtfully balances the intuition, the arithmetic examples, as well the rigorous math details. It can serve both as a textbook, as well as a reference book.' Christos Faloutsos, Carnegie Mellon University, Pennsylvania, and winner of the ACM SIGKDD Innovation Award

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    Product details

    • Edition: 2nd Edition
    • Publication planned for: January 2020
    • format: Hardback
    • isbn: 9781108473989
    • dimensions: 253 x 177 mm
    • availability: Not yet published - available from January 2020
  • Table of Contents

    1. Data mining and analysis
    Part I. Data Analysis Foundations:
    2. Numeric attributes
    3. Categorical attributes
    4. Graph data
    5. Kernel methods
    6. High-dimensional data
    7. Dimensionality reduction
    Part II. Frequent Pattern Mining:
    8. Itemset mining
    9. Summarizing itemsets
    10. Sequence mining
    11. Graph pattern mining
    12. Pattern and rule assessment
    Part III. Clustering:
    13. Representative-based clustering
    14. Hierarchical clustering
    15. Density-based clustering
    16. Spectral and graph clustering
    17. Clustering validation
    Part IV. Classification:
    18. Probabilistic classification
    19. Decision tree classifier
    20. Linear discriminant analysis
    21. Support vector machines
    22. Classification assessment
    Part V. Regression:
    23. Linear regression
    24. Logistic regression
    25. Neural networks
    26. Deep learning
    27. Regression evaluation.

  • Authors

    Mohammed J. Zaki, Rensselaer Polytechnic Institute, New York
    Mohammed J. Zaki is Professor of Computer Science at Rensselaer Polytechnic Institute, New York, where he also serves as Associate Department Head and Graduate Program Director. He has more than 250 publications and is an Associate Editor for the journal Data Mining and Knowledge Discovery. He is on the Board of Directors for Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD). He has received the National Science Foundation CAREER Award, and the Department of Energy Early Career Principal Investigator Award. He is an ACM Distinguished Member, and IEEE Fellow.

    Wagner Meira, Jr, Universidade Federal de Minas Gerais, Brazil
    Wagner Meira, Jr is Professor of Computer Science at Universidade Federal de Minas Gerais, Brazil, where he is currently the chair of the department. He has published more than 230 papers on data mining and parallel and distributed systems. He was leader of the Knowledge Discovery research track of InWeb and is currently Vice-chair of INCT-Cyber. He is on the editorial board of the journal Data Mining and Knowledge Discovery and was the program chair of SDM'16 and ACM WebSci'19. He has been a CNPq researcher since 2002. He has received an IBM Faculty Award and several Google Faculty Research Awards.

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