Visual Data Mining: Techniques and Tools for Data Visualization and Mining / Edition 1

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Overview

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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Editorial Reviews

From The Critics
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
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Product Details

  • ISBN-13: 9780471149996
  • Publisher: Wiley
  • Publication date: 5/30/2002
  • Edition number: 1
  • Pages: 424
  • Product dimensions: 9.25 (w) x 7.50 (h) x 0.86 (d)

Meet 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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Table of Contents

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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Sort by: Showing all of 3 Customer Reviews
  • Anonymous

    Posted August 24, 2002

    This book tells you how to do data mining

    Great book. This book tells you exactly how to do data mining. From how to map business questions on to data mining tasks to how to deploy and monitor data mining models. The various other books on data mining are good for understanding the maths behind the algorithms, but didn't tell me how to use them. This book does this.

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  • Anonymous

    Posted July 13, 2002

    Terrific Book

    To my knowledge this is the only book on data mining that takes you through all the steps of the data mining cycle. The authors have clearly done data mining in the real world and understand that data preparation and model deployment and monitoring are just as important to the success of a project as is building the most accurate model. Highly recommended. The books is applicable to most data mining projects, not just those centered around visualization.

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  • Anonymous

    Posted July 1, 2002

    A very useful book ...

    This is a very useful book on how to achieve a successful data mining project. It details 8 steps in a data mining project and how visualization can play a role in each. Mercifully it covers more than just algorithms and spends 3 chapters on data preparation, 2 chapters on model verification and deploymment.

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