Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration (The Morgan Kaufmann Series in Data Management Systems) [Paperback]

Earl Cox (Author)

Book Description

Publication Date: January 2005 | ISBN-10: 0121942759 | ISBN-13: 978-0121942755 | Edition: 1

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you'll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems.

You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.

* Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems.
* Helps you to understand the trade-offs implicit in various models and model architectures.
* Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction.
* Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model.
* In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem.
* Presents examples in C, C++, Java, and easy-to-understand pseudo-code.
* Extensive online component, including sample code and a complete data mining workbench.

Editorial Reviews

From the Back Cover

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you'll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems.

You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.

Features:
* Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems.
* Helps you to understand the trade-offs implicit in various models and model architectures.
* Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction.
* Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model.
* In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem.
* Presents examples in C, C++, Java, and easy-to-understand pseudo-code.
* Extensive online component, including sample code and a complete data mining workbench.

About the Author:
Earl Cox is the founder and president of Scianta Intelligence, a next-generation machine intelligence and knowledge exploration company. He is a futurist, author, management consultant, and educator dedicated to the epistemology of advanced intelligent systems, the redefinition of the machine mind, and the ways in which evolving and inter-connected virtual worlds affect the sociology of business and culture. He is a recognized expert in fuzzy logic and adaptive fuzzy systems and a pioneer in the integration of fuzzy neural systems with genetic algorithms and case-based reasoning.

About the Author

Earl founded and serves as President of, Scianta Intelligence, a next generation machine intelligence and knowledge exploration company. He is a futurist, author, management consultant, and educator involved in discovering the epistemology of advanced intelligent systems, the redefinition of the machine mind, and, as a pioneer of Internet-based technologies, the way in which evolving inter-connected virtual worlds will affect the sociology of business and culture in the near and far future. Earl has over thirty years experience in managing and participating in the software development process at the system as well as tightly integrated application level. In the area of advanced machine intelligence technologies, Earl is a recognized expert in fuzzy logic, and adaptive fuzzy systems as they are applied to information and decision theory. He has pioneered the integration of fuzzy neural systems with genetic algorithms and case-based reasoning. As an industry observer and futurist, Earl has written and talked extensively on the philosophy of the Response to Change, the nature of Emergent Intelligence, and the Meaning of Information Entropy in Mind and Machine.

Product Details

• Paperback: 530 pages
• Publisher: Morgan Kaufmann; 1 edition (January 2005)
• Language: English
• ISBN-10: 0121942759
• ISBN-13: 978-0121942755
• Product Dimensions: 9.2 x 7.4 x 1.2 inches
• Shipping Weight: 2.4 pounds (View shipping rates and policies)
• Average Customer Review: 2.2 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #2,014,683 in Books (See Top 100 in Books)
  • #41 in Books > Computers & Technology > Programming > Algorithms > Genetic

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Customer Reviews

Most Helpful Customer Reviews

8 of 8 people found the following review helpful:

3.0 out of 5 stars 4 stars for theory, but 3 stars for actual implementation details, September 12, 2005

By 

John W. Conwell - See all my reviews
(REAL NAME)   

This review is from: Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration (The Morgan Kaufmann Series in Data Management Systems) (Paperback)

4 stars for theory, but 3 stars for actual implementation details

This book is a great introduction into Fuzzy Logic, Rules, Sets, and Modeling. The author doesn't assume the reader already has a Masters degree in Comp Sci or Software Eng, and gives good explanations of all the requisite knowledge needed to understand the basics of Fuzzy stuff. For this I gave the author 4 stars.

But, there is very little implementation details in this book. Its mostly theory with a smattering of code here and there. That's where it falls short of invaluable. If the author has made the book a hundred or two more pages longer and went into in depth implementation details this book would have been an easy 5 stars, coving the basics all the way through real world usage and implementation.

As it is, its just a good masters level text book. A good supplemental theory text to read prior to another book that shows more implementation details (which I haven't found yet).

13 of 17 people found the following review helpful:

1.0 out of 5 stars One of the Worst Books Ever -- Deserves Negative Stars, September 21, 2005

By 

Badly Disappointed - See all my reviews

This review is from: Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration (The Morgan Kaufmann Series in Data Management Systems) (Paperback)

Both the author and publisher should be ashamed of releasing this book, and having the gall to charge $50 for it. Let me list some of the ways this book is a rip-off:

1) It was never proof-read or copy edited. There are typographic errors, misspellings, and missing words in the middle of sentences.

2) One chapter (I'll let you find it) skips a number in the figure sequence.

3) Variable names in the text differ from those in the referenced figure.

4) Code fragments in several languages are useless other than to boost the author's ego: look how many computer languages I can use!

5) The examples are all over the map; there's no consistency. Worst of all, however, is that the entire third part of the book (the Genetic Algorithms included in the title) focuses on a crew scheduling algorithm and never shows how that relates to data mining or anything else that preceeded it in the first two parts.

The book looks (and reads) like it was thrown together overnight from previously written materials and no one took the time to make sure it was done correctly. It must have been a stream-of-consciousness core dump without any thoughtful review to see if all parts were necessary, complete, and fit together to make a whole story.

They should pay the reader to take it out of inventory. It is unfortunate that the book is so poor as to be useless, I'm sure that others are as disappointed as I am.

3 of 4 people found the following review helpful:

4.0 out of 5 stars nice match of fuzzy logic and genetic algorithms, October 15, 2005

By 

W Boudville (Terra, Sol 3) - See all my reviews
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This review is from: Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration (The Morgan Kaufmann Series in Data Management Systems) (Paperback)

Cox explains fuzzy modelling at a level that should be readily understandable to many readers. The math ideas are given, but you are not overwhelmed with page after page of formulae. Enough maths is developed to use against data and to produce sensible results.

A good feature of the book is how to apply fuzzy logic against data in a SQL database. Other books on fuzzy logic often ignore this important practical case.

Cluster is decently covered. This is a field where often what is a cluster can be very subjective. Which makes it well suited for a fuzzy approach.

The book also shows a very natural fit between fuzzy logic and genetic algorithms. The probing nature of the latter, where you really don't know what the "proper" answer should be, means that using a fuzzy match as a step within the genetic searching can be a fruitful implementation.