TY  - BOOK
AU  - Olson, David
AU  - Shi, Yong
TI  - Introduction to business data mining
SN  - 007-124470-0
AV  - 006.312 Ol84i 
PY  - 2007///
CY  - New York
PB  - McGraw-Hill
KW  - Business
KW  - Data processing
KW  - Data mining
N1  - Includes index; 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
N2  - 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
ER  -