Table of contents

  1. Front Matter
    Pages i-xxix
  2. Charu C. Aggarwal
    Pages 1-26
  3. Charu C. Aggarwal
    Pages 27-62
  4. Charu C. Aggarwal
    Pages 63-91
  5. Charu C. Aggarwal
    Pages 93-133
  6. Charu C. Aggarwal
    Pages 135-152
  7. Charu C. Aggarwal
    Pages 153-204
  8. Charu C. Aggarwal
    Pages 205-236
  9. Charu C. Aggarwal
    Pages 237-263
  10. Charu C. Aggarwal
    Pages 265-283
  11. Charu C. Aggarwal
    Pages 285-344
  12. Charu C. Aggarwal
    Pages 345-387
  13. Charu C. Aggarwal
    Pages 389-427
  14. Charu C. Aggarwal
    Pages 429-455
  15. Charu C. Aggarwal
    Pages 457-491
  16. Charu C. Aggarwal
    Pages 493-529
  17. Charu C. Aggarwal
    Pages 531-555
  18. Charu C. Aggarwal
    Pages 557-587
  19. Charu C. Aggarwal
    Pages 589-617
  20. Charu C. Aggarwal
    Pages 619-661
  21. Charu C. Aggarwal
    Pages 663-693
  22. Back Matter
    Pages 695-734

About this book


This 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 Chicago


Cluster analysis Data analysis Data analytics Data applications Data mining Data streams Frequent pattern mining Graph mining Outlier analysis Privacy-preserving data mining Sequence mining Social networks Spatial data mining Text mining Time-series analysis Web mining

Authors and affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

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