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Data Mining and Analysis
Fundamental Concepts and Algorithms

£47.99

textbook
  • Date Published: July 2014
  • availability: In stock
  • format: Hardback
  • isbn: 9780521766333

£ 47.99
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  • The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.

    • Provides a solid foundation in data mining, allowing the reader to go beyond the techniques covered in the book
    • Includes broad coverage of data mining sub-areas
    • Provides an algorithmic approach to data mining
    • Intended for both undergraduate and graduate students, as well as researchers and practitioners
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    Product details

    • Date Published: July 2014
    • format: Hardback
    • isbn: 9780521766333
    • length: 562 pages
    • dimensions: 260 x 183 x 31 mm
    • weight: 1.2kg
    • contains: 186 b/w illus. 85 tables 130 exercises
    • availability: In stock
  • 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.

  • Resources for

    Data Mining and Analysis

    Mohammed J. Zaki, Wagner Meira, Jr

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  • Authors

    Mohammed J. Zaki, Rensselaer Polytechnic Institute, New York
    Mohammed J. Zaki is a Professor of Computer Science at Rensselaer Polytechnic Institute. He received his PhD in computer science from the University of Rochester in 1998. His research interests focus on developing novel data mining techniques, especially for applications in bioinformatics and social networks. He has published over 225 papers and book chapters on data mining and bioinformatics, and is the founding co-chair for the BIOKDD series of workshops. He is currently Area Editor for Statistical Analysis and Data Mining, and an Associate Editor for Data Mining and Knowledge Discovery, ACM Transactions on Knowledge Discovery from Data, and Social Network Analysis and Mining. He was the program co-chair for SDM'08, SIGKDD'09, PAKDD'10, BIBM'11, CIKM'12, and ICDM'12. He is currently serving on the Board of Directors for ACM SIGKDD. He received the National Science Foundation CAREER Award in 2001 and the Department of Energy Early Career Principal Investigator Award in 2002. He received an HP Innovation Research Award in 2010, 2011, and 2012, and a Google Faculty Research Award in 2011. He is a senior member of the IEEE, and an ACM Distinguished Scientist. His research is supported in part by NSF, NIH, DOE, Google, HP, and Nvidia.

    Wagner Meira, Jr, Universidade Federal de Minas Gerais, Brazil
    Wagner Meira, Jr is a Professor of Computer Science at the Universidade Federal de Minas Gerais, Brazil.

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