Visual Data Mining W/Ws

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

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

Booknews

Describes how to prepare and transform raw business data into business data sets, then use data visualization and visual data mining techniques to analyze the prepared data sets. The data visualization tools include bar graphs, histograms, pie charts, and tree graphs. Among the data mining tools discussed are decision trees, linear regression models, and self-organizing maps. A customer retention case study illustrates the entire process. Annotation c. Book News, Inc., Portland, OR

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.