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[N482.Ebook] Download Visual Data Mining: Techniques and Tools for Data Visualization and Mining, by Tom Soukup, Ian Davidson

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Visual Data Mining: Techniques and Tools for Data Visualization and Mining, by Tom Soukup, Ian Davidson

Visual Data Mining: Techniques and Tools for Data Visualization and Mining, by Tom Soukup, Ian Davidson



Visual Data Mining: Techniques and Tools for Data Visualization and Mining, by Tom Soukup, Ian Davidson

Download Visual Data Mining: Techniques and Tools for Data Visualization and Mining, by Tom Soukup, Ian Davidson

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Visual Data Mining: Techniques and Tools for Data Visualization and Mining, by Tom Soukup, Ian Davidson

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

  • Sales Rank: #3139617 in Books
  • Published on: 2002-05-30
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.20" h x .91" w x 7.60" l, 1.60 pounds
  • Binding: Paperback
  • 424 pages

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.

Most helpful customer reviews

4 of 4 people found the following review helpful.
Very nice book ...
By A Customer
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.

2 of 3 people found the following review helpful.
Highly recommended
By A Customer
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.

3 of 3 people found the following review helpful.
different type of data mining book
By A Customer
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.

See all 6 customer reviews...

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