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

Tom Soukup (Author), Ian Davidson (Author)
4.5 out of 5 stars  See all reviews (6 customer reviews)
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Product Description

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

Book Info

Describes how various types of business problems can be solved using visual mining techniques. Presents a step by step methodology for implementing visual mining techniques into your own business intelligence project. Softcover.
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Inside This Book (Learn More)
First Sentence
When you read a newspaper or magazine, or watch a news or weather program on TV, you see numerous data visualizations. Read the first page
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Concordance
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews

6 Reviews
5 star:
 (3)
4 star:
 (3)
3 star:    (0)
2 star:    (0)
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Average Customer Review
4.5 out of 5 stars (6 customer reviews)
 
 
 
 
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4.0 out of 5 stars A nice applied Data Mining Book, Aug 24 2002
By 
Craig Nubik (San Bruno, CA, USA) - See all my reviews
This review is from: Visual Data Mining: Techniques and Tools for Data Visualization and Mining (Paperback)
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.

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5.0 out of 5 stars Highly recommended, Aug 22 2002
This review is from: Visual Data Mining: Techniques and Tools for Data Visualization and Mining (Paperback)
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.

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4.0 out of 5 stars different type of data mining book, Jun 8 2002
By 
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.

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