Introduction to business data mining / David Olson.

By: Contributor(s): Material type: TextTextPublication details: New York : McGraw-Hill, 2007.Description: xiii, 273 p; 26 cmContent type:
  • Text
Media type:
  • Unmediated
Carrier type:
  • Volume
ISBN:
  • 007-124470-0
Subject(s): LOC classification:
  • 006.312 Ol84i
Contents:
Part I: INTRODUCTION 1: Initial Description of Data Mining in Business 2: Data Mining Processes and Knowledge Discovery 3: Database Support to Data Mining Part II: DATA MINING METHODS AS TOOLS 4: Overview of Data Mining Techniques 4 Appendix: Enterprise Miner Demonstration on Expenditure Data Set 5: Cluster Analysis 5 Appendix: Clementine 6: Regression Algorithms in Data Mining 7: Neural Networks in Data Mining 8: Decision Tree Algorithms 8 Appendix: Demonstration of See5 Decision Tree Analysis 9: Linear Programming-Based Methods 9 Appendix: Data Mining Linear Programming Formulations Part III: BUSINESS APPLICATIONS 10: Business Data Mining Applications Applications 11: Market-Basket Analysis 11 Appendix: Market-Basket Procedure Part IV: DEVELOPING ISSUES 12: Text and Web Mining 12 Appendix: Semantic Text Analysis 13: Ethical Aspects of Data Mining
Summary: Introduces students to the fundamental concepts of data mining. This text shows readers how to gather and analyze large sets of data to gain useful business understanding. It also describes and demonstrated basic data mining algorithms, and presents an overview of the developing areas in this field, including web mining, text mining, and more.
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Holdings
Item type Current library Call number Status Barcode
Books Books FEU - Cavite Higher Education HED Library 006.312 Ol84i (Browse shelf(Opens below)) Available HED2579

Includes index

English

Part I: INTRODUCTION 1: Initial Description of Data Mining in Business 2: Data Mining Processes and Knowledge Discovery 3: Database Support to Data Mining Part II: DATA MINING METHODS AS TOOLS 4: Overview of Data Mining Techniques 4 Appendix: Enterprise Miner Demonstration on Expenditure Data Set 5: Cluster Analysis 5 Appendix: Clementine 6: Regression Algorithms in Data Mining 7: Neural Networks in Data Mining 8: Decision Tree Algorithms 8 Appendix: Demonstration of See5 Decision Tree Analysis 9: Linear Programming-Based Methods 9 Appendix: Data Mining Linear Programming Formulations Part III: BUSINESS APPLICATIONS 10: Business Data Mining Applications Applications 11: Market-Basket Analysis 11 Appendix: Market-Basket Procedure Part IV: DEVELOPING ISSUES 12: Text and Web Mining 12 Appendix: Semantic Text Analysis 13: Ethical Aspects of Data Mining

Introduces students to the fundamental concepts of data mining. This text shows readers how to gather and analyze large sets of data to gain useful business understanding. It also describes and demonstrated basic data mining algorithms, and presents an overview of the developing areas in this field, including web mining, text mining, and more.

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