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Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner / Galit Shmueli

By: Shmueli, GalitContributor(s): Shmueli, Galit | Bruce, Peter C | Patel, Nitin R | Torgo, LuísMaterial type: Text

TextPublication details: New Jersey : John Wiley & Sons, 2007Description: xviii, 279 pages : illustrations ; 26 cmISBN: 9788126517589Subject(s): Computer science, information & general works | Computer programming, programs & data | Microsoft Excel (Computer file) | Data mining | Dataprocessing | Statistiek | Business -- Data processingDDC classification: 005.54 SHM

Contents:

Introduction -- Overview of the data mining process -- Data exploration and dimension reduction -- Evaluating classification and predictive performance -- Multiple linear regression -- Three simple classification methods -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Association rules -- Cluster analysis -- Cases.

Summary: Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	SNU LIBRARY	005.54 SHM (Browse shelf(Opens below))	Available		G1007

Total holds: 0

Introduction --
Overview of the data mining process --
Data exploration and dimension reduction --
Evaluating classification and predictive performance --
Multiple linear regression --
Three simple classification methods --
Classification and regression trees --
Logistic regression --
Neural nets --
Discriminant analysis --
Association rules --
Cluster analysis --
Cases.

Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success

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