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Foundations of Computational Intelligence Volume 4

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Cover of 'Foundations of Computational Intelligence Volume 4'

Table of Contents

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    Book Overview
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    Chapter 1 Adaptive and Self-adaptive Techniques for Evolutionary Forecasting Applications Set in Dynamic and Uncertain Environments
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    Chapter 2 Sequence Pattern Mining
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    Chapter 3 Growing Self-Organizing Map for Online Continuous Clustering
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    Chapter 4 Synthesis of Spatio-temporal Models by the Evolution of Non-uniform Cellular Automata
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    Chapter 5 Genetic Selection Algorithm and Cloning for Data Mining with GMDH Method
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    Chapter 6 Inducing Relational Fuzzy Classification Rules by Means of Cooperative Coevolution
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    Chapter 7 Post-processing Evolved Decision Trees
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    Chapter 8 Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues
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    Chapter 9 Stability-Based Model Order Selection for Clustering Using Multiple Cooperative Particle Swarms
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    Chapter 10 Foundations of Computational Intelligence Volume 4
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    Chapter 11 A Clustering Genetic Algorithm for Genomic Data Mining
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    Chapter 12 Detection of Remote Protein Homologs Using Social Programming
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    Chapter 13 Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System
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    Chapter 14 Web Data Clustering
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    Chapter 15 Efficient Construction of Image Feature Extraction Programs by Using Linear Genetic Programming with Fitness Retrieval and Intermediate-Result Caching
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    Chapter 16 Mining Network Traffic Data for Attacks through MOVICAB-IDS
Attention for Chapter 9: Stability-Based Model Order Selection for Clustering Using Multiple Cooperative Particle Swarms
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Chapter title
Stability-Based Model Order Selection for Clustering Using Multiple Cooperative Particle Swarms
Chapter number 9
Book title
Foundations of Computational Intelligence Volume 4
Published by
Springer, Berlin, Heidelberg, January 2009
DOI 10.1007/978-3-642-01088-0_9
Book ISBNs
978-3-64-201087-3, 978-3-64-201088-0
Authors

Abbas Ahmadi, Fakhri Karray, Mohamed S. Kamel

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 33%
Professor > Associate Professor 1 33%
Researcher 1 33%
Readers by discipline Count As %
Engineering 2 67%
Unknown 1 33%