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Visual Data Mining: Techniques and Tools for Data Visualization and Mining By Tom Soukup, Ian Davidson
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- Amazon Sales Rank: #3642529 in Books
- Published on: 2002-05-30
- Original language: English
- Number of items: 1
- Dimensions: 9.20" h x .91" w x 7.60" l, 1.60 pounds
- Binding: Paperback
- 424 pages
From the Back Cover Master the power of visual data mining tools and techniques By harnessing the power of visual data mining tools and techniques, business analysts can quickly and easily retrieve information to solve common business problems from an entirely new perspective. Traditional data mining techniques generate huge amounts of numeric data that can be difficult to interpret and use. Visual data mining makes it easier for nontechnical business managers to understand their markets and make savvy business decisions, in addition to opening the world of visual tools to a much broader audience. This book describes how various types of business problems can be solved using visual mining techniques. After introducing the business issues and fundamentals, it then presents a step-by-step methodology for implementing visual mining techniques into your own business intelligence project. This methodology will explain how to: * Justify and plan your project * Identify your top business questions and map them into data visualization or data mining questions * Identify and select data to address these questions * Transform raw data into a visualization or data mining business data set and verify for accuracy * Choose the appropriate visualization or visual data mining tool * Analyze and evaluate the visualizations or data mining models to discover business insights, trends, and anomalies The companion Web site includes: * Links to data visualization and visual data mining tools * Links to real-world success stories using visual data mining * The major example data sets used throughout the book
About the Author 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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