[推荐]Data Mining Techniques : For Marketing, Sales, and Customer Relationship Man - 第4页 - 计量经济学与统计软件

yinjb 发表于 2005-1-19 00:23:00 |显示全部楼层 |坛友微信交流群

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hanszhu 发表于 2005-1-19 03:06:00 |显示全部楼层 |坛友微信交流群

I am trying to upload the following book:

Visual Data Mining: Techniques and Tools for Data Visualization and Mining by Tom Soukup, Ian Davidson, Tom Soukup, Ian Davidson

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From Book News, Inc. 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.Book News, Inc.®, Portland, OR Product Description: 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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hanszhu 发表于 2005-1-19 03:10:00 |显示全部楼层 |坛友微信交流群

Visual Data Mining: Techniques and Tools for Data Visualization and Mining Tom Soukup, Ian Davidson ISBN: 0-471-14999-3 Paperback 416 pages May 2002 CDN $71.99

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.

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.