Customer Reviews

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

5 star:		(3)
4 star:		(3)
3 star:		(0)
2 star:		(0)
1 star:		(0)

 

 

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.

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!

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.

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.

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.

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.