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Visual Data Mining: Techniques and Tools for Data Visualization and Mining
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Author: Tom Soukup, Ian Davidson
List Price: $50.00
Our Price: Click to see the latest and low price
ISBN: 0471149993
Publisher: John Wiley & Sons (15 May, 2002)
Edition: Paperback
Sales Rank: 458,048
Average Customer Rating: 4.5 out of 5
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Customer ReviewsRating: 4 out of 5
A nice applied Data Mining Book
In my opinion, there are two types of data
mining books.The first type such as by Hand et' al, Han,
Witten etc focus on the techniques. The second type which this book falls into
focuses on how to apply the techniques.
I like this book more than other books
of the same type such as the one by
Herb Edelstein because it has a detailed
case study that is built upon throughout
the book. This book is a good example of how to apply
data mining. It is obvious the authors have
done data mining in industry, otherwise they
wouldn't have a section in the book on:
"Mapping Business Questions To Data Mining
Tasks". Highly recommended.
Rating: 5 out of 5
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.
Rating: 4 out of 5
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 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.
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