Data Mining: Know It All


  • Soumen Chakrabarti, Asst. Prof. of Computer Science, Indian Institute of Technology, Bombay
  • Earl Cox, Scianta Intelligence, LLC, Chapel Hill, NC
  • Eibe Frank, University of Waikato, Hamilton, New Zealand. Recipient of the 2005 ACM SIGKDD Service Award.
  • Ralf Güting
  • Jiawei Han, University of Illinois, Urbana Champaign
  • Xia Jiang, University of Pittsburgh, PA, USA
  • Micheline Kamber, Simon Fraser University, Burnaby, Canada
  • Sam Lightstone, IBM, Toronto, Canada
  • Thomas Nadeau
  • Richard E. Neapolitan, Northeastern Illinois University, Chicago, USA
  • Dorian Pyle, PTI, Leominster
  • Mamdouh Refaat, Consultant
  • Markus Schneider, University of Florida at Gainesville
  • Toby Teorey, University of Michigan, Ann Arbor, USA
  • Ian Witten, University of Waikato, Hamilton, New Zealand.

This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.
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Data analysts, Data modelers, Database R&D professionals, data warehouse engineers, data mining professionals, undergraduate and graduate students who want to incorporate data mining as part of their data management knowledge base and expertise.


Book information

  • Published: November 2008
  • ISBN: 978-0-12-374629-0

Table of Contents

Chapter 1: Data Mining Overview Chapter 2: Data Acquisition and Integration Chapter 3: Data Pre-processing Chapter 4: Physical Design for Decision Support, Warehousing, and OLAPChapter 5: Algorithms - The Basic Methods Chapter 6: Further Techniques in Decision Analysis Chapter 7: Fundamental Concepts of Genetic Algorithms Chapter 8: Spatio-Temporal Data Structures and Algorithms for Moving Objects Types Chapter 9: Improving the Mined ModelChapter 10: Web Mining - Social Network Analysis