Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner / by Galit Shmueli, Nitin R. Patel, Peter C. Bruce.

By: Shmueli, Galit, 1971-Contributor(s): Patel, Nitin R. (Nitin Ratilal) | Bruce, Peter C, 1953-Material type: TextTextPublisher: New Delhi. : Wiley-Interscience, c2007Description: xviii, 279 p. : ill. ; 26 cmISBN: 9788126517589 (pbk)Subject(s): Microsoft Excel (Computer file) | Business -- Data processing | Data mining | Business intelligenceDDC classification: 005.54 LOC classification: HF5548.2 | .S44843 2007
Contents:
CHAPTER 1: Introduction ---------------------- CHAPTER 2: Overview of data mining process --------------------- CHAPTER 3: Data exploration and Dimension reduction ------------------------- CHAPTER 4: Evaluating Classification and predictive performance ---------------------- CHAPTER 5: Multiple linear regression --------------------- CHAPTER 6: Three simple classification method --------------------- CHAPTER 7: Classification and regression trees ---------------------- CHAPTER 8: Logistic regression ---------------------- CHAPTER 9: Neural nets ------------------- CHAPTER 10: Discriminant analysis --------------------- CHAPTER 11: Association rules ----------------------- CHAPTER 12: Cluster analysis ----------------------
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Item type Current location Call number Status Date due Barcode Item holds
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Computer Science
005.54 SHM-D 2012 4375 (Browse shelf) Available 0004375
Total holds: 0

Includes bibliographical references (p. 271-272) and index.

CHAPTER 1: Introduction ----------------------
CHAPTER 2: Overview of data mining process ---------------------
CHAPTER 3: Data exploration and Dimension reduction -------------------------
CHAPTER 4: Evaluating Classification and predictive performance ----------------------
CHAPTER 5: Multiple linear regression ---------------------
CHAPTER 6: Three simple classification method ---------------------
CHAPTER 7: Classification and regression trees ----------------------
CHAPTER 8: Logistic regression ----------------------
CHAPTER 9: Neural nets -------------------
CHAPTER 10: Discriminant analysis ---------------------
CHAPTER 11: Association rules -----------------------
CHAPTER 12: Cluster analysis ----------------------

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