Data mining techniques : for marketing, sales, and customer relationship management / Gordon S Linoff, Michael J Berry.

By: Linoff, Gordon S
Contributor(s): Berry, Micahel J. A
Publisher: Indianapolis, IN : Wiley Pub., 2011Edition: 3rd edDescription: xl, 847 p. : ill. ; 24 cmISBN: 9780470650936 (pbk : alk. paper)Subject(s): Data mining | Marketing -- Data processing | Business -- Data processingLOC classification: HF5415.125 | .B47 2011
Contents:
What Is data mining and why do it? -- Data mining applications in marketing and customer relationship management -- The data mining process -- Statistics 101: What you should know about data -- Descriptions and prediction: profiling and predictive modeling -- Data mining using classic statistical techniques -- Decision trees -- Artificail neural networks -- Nearest neighbor approaches: Memory-based reasoning and collaborative filtering -- Knowing when to worry: Using survival analysis to understand customers -- Genetic algorithms and swarm intelligence -- Tell me something new: Pattern discovery and data mining -- Finding islands of similarity: Automatic cluster detection -- Alternative approaches to cluster detection -- Market basket analysis and association rules -- Link analysis -- Data warehousing, OLAP, analytic sandboxes, and data mining -- Building customer signatures -- Derived variables: Making the data mean more -- Too much of a good thing? Techniques for reducing the number of variables -- Listen carefully to what your customers say: text mining.
Summary: The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition-more than 50% new and revised- is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
Item type Current location Home library Call number Status Date due Barcode Item holds
BOOK BOOK COLLEGE LIBRARY
COLLEGE LIBRARY
SUBJECT REFERENCE 		658.802 L649 2011 (Browse shelf) Available CITU-CL-42752
Total holds: 0

Berry's name appears first on the 2nd ed.

Includes index.

What Is data mining and why do it? --
Data mining applications in marketing and customer relationship management --
The data mining process --
Statistics 101: What you should know about data --
Descriptions and prediction: profiling and predictive modeling --
Data mining using classic statistical techniques --
Decision trees --
Artificail neural networks --
Nearest neighbor approaches: Memory-based reasoning and collaborative filtering --
Knowing when to worry: Using survival analysis to understand customers --
Genetic algorithms and swarm intelligence --
Tell me something new: Pattern discovery and data mining --
Finding islands of similarity: Automatic cluster detection --
Alternative approaches to cluster detection --
Market basket analysis and association rules --
Link analysis --
Data warehousing, OLAP, analytic sandboxes, and data mining --
Building customer signatures --
Derived variables: Making the data mean more --
Too much of a good thing? Techniques for reducing the number of variables --
Listen carefully to what your customers say: text mining.

The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition-more than 50% new and revised- is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems.

There are no comments for this item.

to post a comment.