Data mining techniques : for marketing, sales, and customer relationship management

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Title:

Author:

Berry, Michael J. A.

ISBN:

9780471470649

Personal Author:

Berry, Michael J. A.

Edition:

2nd ed.

Publication Information:

Indianapolis, Ind. : Wiley, c2004.

Physical Description:

xxv, 643 p. : ill., 1 map ; 24 cm.

General Note:

Includes index.

Contents:

Why and what -- The virtuous cycle of data mining -- Data mining methodology and best practices -- Business applications of data mining -- Data mining with familiar tools -- Decision trees -- Neural networks -- Nearest neighbor approaches : memory based reasoning and collaborative filtering -- Association rules -- Link analysis -- Clustering -- Survival analysis -- Genetic algorithms -- Finding customers in data -- Data mining, data warehousing, and OLAP -- The data mining environment -- Whipping the data into shape -- Putting data mining to work.

Subject Term:

Data mining.

Marketing -- Data processing.

Business -- Data processing.

Added Author:

Linoff, Gordon.

Electronic Access:

Publisher description http://www.loc.gov/catdir/description/wiley041/2003026693.html

Copies:

Available:*

Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... 1:IIEMSA	1:GEN-BOOK	33168023674373	658.802 B534D 2004	1	Searching... Unknown

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Summary

Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis