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

By: Contributor(s): Material type: TextTextPublication details: Indianapolis, Ind. : Wiley Pub., ©2004.Edition: 2nd edDescription: xxv, 643 p. : ill. ; 24 cmISBN:
  • 8126505176
Subject(s): DDC classification:
  • 006.3 22 BER-D
LOC classification:
  • HF5415.125 .B47 2004
Online resources:
Contents:
Why and what is data mining? -- The virtuous cycle of data mining -- Data mining methodology and best practices -- Data mining applications in marketing and customer relationship management -- The lure of statistics: data mining using familiar tools -- Decision trees -- Artificial neural networks -- Nearest neighbor approaches : memory-based reasoning and collaborative filtering -- Market basket analysis and association rules -- Link analysis -- Automatic Cluster detection -- Knowing when to worry: hazard functions and survival analysis in marketing -- Genetic algorithms -- Data mining throughout the customer life cycle -- Data warehousing, OLAP, and data mining -- Building the data mining environment -- Preparing data for mining -- Putting data mining to work.
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Holdings
Item type Current library Collection Call number Status Notes Date due Barcode Item holds
Books Books IIITD General Stacks Computer Science and Engineering 006.3 BER-D (Browse shelf(Opens below)) Available Gifted by Prof. Pankaj Jalote G00065
Total holds: 0

Includes index.

Why and what is data mining? -- The virtuous cycle of data mining -- Data mining methodology and best practices -- Data mining applications in marketing and customer relationship management -- The lure of statistics: data mining using familiar tools -- Decision trees -- Artificial neural networks -- Nearest neighbor approaches : memory-based reasoning and collaborative filtering -- Market basket analysis and association rules -- Link analysis -- Automatic Cluster detection -- Knowing when to worry: hazard functions and survival analysis in marketing -- Genetic algorithms -- Data mining throughout the customer life cycle -- Data warehousing, OLAP, and data mining -- Building the data mining environment -- Preparing data for mining -- Putting data mining to work.

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