Data Mining: Know It All

All of the elements of data mining together in a single volume written by the best and brightest experts in the field!

Soumen Chakrabarti (Author), Richard E. Neapolitan (Author), Dorian Pyle (Author), Mamdouh Refaat (Author), Markus Schneider (Author), Toby J. Teorey (Author), Ian H. Witten (Author), Earl Cox (Author), Eibe Frank (Author), Ralf Hartmut Güting (Author), Jiawei Han (Author), Xia Jiang (Author), Micheline Kamber (Author), Sam S. Lightstone (Author), Thomas P. Nadeau (Author)

9780123746290, Elsevier Science

Hardback, published 27 November 2008

480 pages
23.4 x 19 x 2.9 cm, 1.14 kg

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.

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

Subject Areas: Machine learning [UYQM], Data mining [UNF], Library, archive & information management [GLC]