Visual Data Mining

TOM SOUKUP has more than fifteen years of experience in data management and analysis. He is currently with Konami Gaming, Inc., where he is involved in data mining and data warehousing projects for the gaming industry. IAN DAVIDSON, PhD, has worked on commercial data mining applications, including insurance claim fraud detection, product cross-sell, customer retention, and credit card fraud detection. He is currently an Assistant Professor of Computer Science at the State University of New York, Albany.

Introduction. Acknowledgments. Trademarks. PART 1: INTRODUCTION AND PROJECT PLANNING PHASE. Introduction to Data Visualization and Visual Data Mining. Step 1: Justifying and Planning the Data Visualization and Data Mining Project. Step 2: Identifying the Top Business Questions. PART 2: DATA PREPARATION PHASE. Step 3: Choosing the Business Data Set. Step 4: Transforming the Business Data Set. Step 5: Verify the Business Data Set. PART 4: DATA ANALYSIS PHASE AND SUMMARY. Step 6: Choosing the Visualization or Visual Mining Tool. Step 7: Analyzing the Visualization or Mining Tool. Step 8: Verifying and Presenting the Visualizations or Mining Models. The Future of Visual Data Mining. Glossary. References. Index.