Visual Data Mining: Techniques and Tools for Data Visualization and Mining

by Ian Davidson and Tom Soukup

Book 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...

• Format: Paperback
• Language: English
• ISBN: 0471149993
• ISBN13: 9780471149996
• Release Date: June 2002
• Publisher: Wiley
• Length: 424 Pages
• Weight: 1.70 lbs.
• Dimensions: 1.0" x 7.5" x 9.2"

Customer Reviews

Highly recommended

I believe Stephen Eick, Cheif Technology Officer of Visual Insights best put into words in the Advance Praise section of the book that "This book is a wonderful contribution and important resource for anyone building visual data mining systems. It combines down-to-earth, practical advice with thoughtful examples."In addition, Michael Berry of Data Miners, Inc states "As this book shows, visualization plays an important role in every step of the data mining process. Soukup and Davidson take the reader through every detail of this process, providing sample SQL code for each practical example. In fact, much of their advice on project planning and data extract, transformation and cleaning is applicable to all data mining projects, visual or not."I found the eight step VDM methodology applicable to data mining my own data. Highly recommended.

different type of data mining book

Most data mining books focus on the algorithms.This book takes a different tack. It discusses using the algorithms and visualization within a data miningproject. Alot of the book focuses on the "darkerside" of data mining: data preparation, model performance and deploying your model once it isbuilt and tested. There are two chapters onalgorithms but they mainly focus on how to visualizethe model, its performance, expected vs actual performance.The book is well written and easy to follow. Thehighly detailed retention case study is a nice addition.One small critisim is that the authors get a littleto much on a soap box when discussing how to justifyto management a data mining project.

Find the Right Tool

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!

Solid All-Around Coverage

The examples are terrific, including those in color---and the business-oriented examples and cases are practical and detailed. The author also does a good job of covering tough areas, like verifying accuracy of visualizations, and selecting the correct data sets for analysis.

Very nice book ...

This is a nice book on the key steps of a data miningproject and the role visualization can play, thoughit 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 projectsuch as data transformation/sampling, building models and deploying models.This book tells you how to *use* data mining and visualizationtools 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 isa nice addition. However, the companion web-site is slowto access.