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Soft Computing for Knowledge Discovery and Data Mining

By: Oded Maimon (Editor), Lior Rokach (Editor)

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Hardcover | 2 November 2007

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Hardcover
448 Pages

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23.39 x 15.6 x 2.54

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Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability.

Soft Computing for Knowledge Discovery and Data Mining introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining. This edited volume by highly regarded authors, includes several contributors of the 2005, Data Mining and Knowledge Discovery Handbook. This book was written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including manufacturing, medical, banking, insurance and others, but also includes various real-world case studies with detailed results.

Soft Computing for Knowledge Discovery and Data Mining is designed for practitioners and researchers in industry. Practitioners and researchers may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a secondary textbook or reference for advanced-level students in information systems, engineering, computer science and statistics management.

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Non-Fiction Computing & I.T.Databases Data Mining Computer Science Artificial Intelligence Machine Learning Computer Networking & Communications Computer Programming & Software Development Software Engineering Computer Vision Computer Hardware

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Paperback

Published: 4th November 2010

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Introduction to Soft Computing for Knowledge Discovery and Data Mining	p. 1
Neural Network Methods
Neural Networks For Data Mining	p. 17
Improved SOM Labeling Methodology for Data Mining Applications	p. 45
Evolutionary Methods
A Review of Evolutionary Algorithms for Data Mining	p. 79
Genetic Clustering for Data Mining	p. 113
Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming	p. 133
Evolutionary Design of Code-matrices for Multiclass Problems	p. 153
Fuzzy Logic Methods
The Role of Fuzzy Sets in Data Mining	p. 187
Support Vector Machines and Fuzzy Systems	p. 205
KDD in Marketing with Genetic Fuzzy Systems	p. 225
Knowledge Discovery in a Framework for Modelling with Words	p. 241
Advanced Soft Computing Methods and Areas
Swarm Intelligence Algorithms for Data Clustering	p. 279
A Diffusion Framework for Dimensionality Reduction	p. 315
Data Mining and Agent Technology: a fruitful symbiosis	p. 327
Approximate Frequent Itemset Mining In the Presence of Random Noise	p. 363
The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining	p. 391
Index	p. 433
Table of Contents provided by Ingram. All Rights Reserved.

Soft Computing for Knowledge Discovery and Data Mining

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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