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

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Overview

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

Author Biography: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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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)

Table of Contents

Acknowledgments
About the Authors
Trademarks
Introduction
Pt. 1 Introduction and Project Planning Phase 1
Ch. 1 Introduction to Data Visualization and Visual Data Mining 3
Ch. 2 Step 1: Justifying and Planning the Data Visualization and Data Mining Project 25
Ch. 3 Step 2: Identifying the Top Business Questions 49
Pt. 2 Data Preparation Phase 65
Ch. 4 Step 3: Choosing the Business Data Set 67
Ch. 5 Step 4: Transforming the Business Data Set 129
Ch. 6 Step 5: Verify the Business Data Set 171
Pt. 3 Data Analysis Phase and Beyond 203
Ch. 7 Step 6: Choosing the Visualization or Visual Mining Tool 205
Ch. 8 Step 7: Analyzing the Visualization or Mining Tool 253
Ch. 9 Step 8: Verifying and Presenting the Visualizations or Mining Models 317
Ch. 10 The Future of Visual Data Mining 339
Glossary 357
References 363
Index 365
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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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