Introduction to Business Data Mining / Edition 1
by David L. Olson, Yong Shi
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… See more details below
Overview
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.
Product Details
- ISBN-13:
- 9780072959710
- Publisher:
- McGraw-Hill Companies, The
- Publication date:
- 11/18/2005
- Edition description:
- New Edition
- Pages:
- 288
- Product dimensions:
- 8.10(w) x 10.30(h) x 0.68(d)
Table of Contents
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 ApplicationsApplications
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
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