Introduction To Business Data Mining

Olson, David L.

ISBN 10: 0072959711 / ISBN 13: 9780072959710

Editore: Irwin Professional Pub, 2005

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Dati bibliografici

• Titolo: Introduction To Business Data Mining
• Editore: Irwin Professional Pub
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 0072959711
• ISBN 13: 9780072959710
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

Introduction to Business Data Mining was developed to introduce students, as opposed to professional practitioners or engineering students, to the fundamental concepts of data mining. Most importantly, this text shows readers how to gather and analyze large sets of data to gain useful business understanding.

A four part organization introduces the material (Part I), describes and demonstrated basic data mining algorithms (Part II), focuses on the business applications of data mining (Part III), and presents an overview of the developing areas in this field, including web mining, text mining, and the ethical aspects of data mining. (Part IV).

The author team has had extensive experience with the quantitative analysis of business as well as with data mining analysis. They have both taught this material and used their own graduate students to prepare the text’s data mining reports. Using real-world vignettes and their extensive knowledge of this new subject, David Olson and Yong Shi have created a text that demonstrates data mining processes and techniques needed for business applications.

Contenuti

Part I: INTRODUCTION

Chapter 1: Initial Description of Data Mining in Business

Chapter 2: Data Mining Processes and Knowledge Discovery

Chapter 3: Database Support to Data Mining

Part II: DATA MINING METHODS AS TOOLS

Chapter 4: Overview of Data Mining Techniques

Chapter 4 Appendix: Enterprise Miner Demonstration on Expenditure Data Set

Chapter 5: Cluster Analysis

Chapter 5 Appendix: Clementine

Chapter 6: Regression Algorithms in Data Mining

Chapter 7: Neural Networks in Data Mining

Chapter 8: Decision Tree Algorithms

Appendix 8: Demonstration of See5 Decision Tree Analysis

Chapter 9: Linear Programming-Based Methods

Chapter 9 Appendix: Data Mining Linear Programming Formulations

Part III: BUSINESS APPLICATIONS

Chapter 10: Business Data Mining Applications Applications

Chapter 11: Market-Basket Analysis

Chapter 11 Appendix: Market-Basket Procedure

Part IV: DEVELOPING ISSUES

Chapter 12: Text and Web Mining

Chapter 12 Appendix: Semantic Text Analysis

Chapter 13: Ethical Aspects of Data Mining