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Visual Data Mining: Techniques and Tools for Data Visualization and Mining

ISBN: 978-0-471-27138-3
416 pages
October 2002
Visual Data Mining: Techniques and Tools for Data Visualization and Mining (0471271381) cover image
Marketing analysts use data mining techniques to gain a reliable understanding of customer buying habits and then use that information to develop new marketing campaigns and products. Visual mining tools introduce a world of possibilities to a much broader and non-technical audience to help them solve common business problems.
  • Explains how to select the appropriate data sets for analysis, transform the data sets into usable formats, and verify that the sets are error-free
  • Reviews how to choose the right model for the specific type of analysis project, how to analyze the model, and present the results for decision making
  • Shows how to solve numerous business problems by applying various tools and techniques
  • Companion Web site offers links to data visualization and visual data mining tools, and real-world success stories using visual data mining
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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.
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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.
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