By
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.
Description
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.
Audience:
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.
