Visual Data Mining: Techniques and Tools for Data Visualization and Mining [Paperback]

Tom Soukup (Author), Ian Davidson (Author)

Book Description

ISBN-10: 0471149993 | ISBN-13: 978-0471149996 | Publication Date: May 15, 2002 | Edition: 1st

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

Editorial Reviews

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.

Product Details

• Paperback: 416 pages
• Publisher: Wiley; 1st edition (May 15, 2002)
• Language: English
• ISBN-10: 0471149993
• ISBN-13: 978-0471149996
• Product Dimensions: 9.1 x 7.5 x 1 inches
• Shipping Weight: 1.7 pounds (View shipping rates and policies)
• Average Customer Review: 4.5 out of 5 stars  See all reviews (6 customer reviews)
• Amazon Best Sellers Rank: #138,794 in Books (See Top 100 in Books)
  • #49 in Books > Computers & Technology > Databases > Data Mining

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Customer Reviews

Most Helpful Customer Reviews

4 of 4 people found the following review helpful:

4.0 out of 5 stars Very nice book ..., June 3, 2002

By 

"johnsmythe" (San Jose, CA, USA) - See all my reviews

This review is from: Visual Data Mining: Techniques and Tools for Data Visualization and Mining (Paperback)

This is a nice book on the key steps of a data mining
project and the role visualization can play, though
it would be useful even if your project doesn't use
visualization.

The book is aimed squarely at the practioner and discusses
in depth each of eight steps in a typical data mining project
such as data transformation/sampling, building models and deploying models.

This book tells you how to *use* data mining and visualization
tools and would be a nice companion to a book on just the
data mining algorithms alone.

The detailed case study used to demonstrate each step is
a nice addition. However, the companion web-site is slow
to access.

3 of 3 people found the following review helpful:

5.0 out of 5 stars Find the Right Tool, June 5, 2002

By A Customer

This review is from: Visual Data Mining: Techniques and Tools for Data Visualization and Mining (Paperback)

I've been doing data mining for years, but have only recently begun working with a visual tool (better left unnamed), that I found frustrating to use. This book has been really helpful in giving me the lay of the land on visual mining techniques and tools, and insight into the right kind of tool for the work I do. Thanks for helping me get on the right track!

3 of 3 people found the following review helpful:

4.0 out of 5 stars different type of data mining book, June 8, 2002

By 

"roberto_cascala" (LA, California) - See all my reviews

This review is from: Visual Data Mining: Techniques and Tools for Data Visualization and Mining (Paperback)

Most data mining books focus on the algorithms.

This book takes a different tack. It discusses
using the algorithms and visualization within a data mining
project. Alot of the book focuses on the "darker
side" of data mining: data preparation, model
performance and deploying your model once it is
built and tested. There are two chapters on
algorithms but they mainly focus on how to visualize
the model, its performance, expected vs actual
performance.

The book is well written and easy to follow. The
highly detailed retention case study is a nice addition.
One small critisim is that the authors get a little
to much on a soap box when discussing how to justify
to management a data mining project.