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Soft Computing for Knowledge Discovery and Data Mining

Overview of attention for book
Cover of 'Soft Computing for Knowledge Discovery and Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Soft Computing for Knowledge Discovery and Data Mining
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    Chapter 2 Neural Networks For Data Mining
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    Chapter 3 Improved SOM Labeling Methodology for Data Mining Applications
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    Chapter 4 A Review of evolutionary Algorithms for Data Mining
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    Chapter 5 Genetic Clustering for Data Mining
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    Chapter 6 Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming
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    Chapter 7 evolutionary Design of Code-matrices for Multiclass Problems
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    Chapter 8 The Role of Fuzzy Sets in Data Mining
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    Chapter 9 Support Vector Machines and Fuzzy Systems
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    Chapter 10 KDD in Marketing with Genetic Fuzzy Systems
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    Chapter 11 Knowledge Discovery in a Framework for Modelling with Words
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    Chapter 12 Swarm Intelligence Algorithms for Data Clustering
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    Chapter 13 A Diffusion Framework for Dimensionality Reduction
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    Chapter 14 Data Mining and Agent Technology: a fruitful symbiosis
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    Chapter 15 Approximate Frequent Itemset Mining In the Presence of Random Noise
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    Chapter 16 The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining
Attention for Chapter 1: Introduction to Soft Computing for Knowledge Discovery and Data Mining
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Citations

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Summary Dimensions citations
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Chapter title
Introduction to Soft Computing for Knowledge Discovery and Data Mining
Chapter number 1
Book title
Soft Computing for Knowledge Discovery and Data Mining
Published by
Springer, Boston, MA, January 2008
DOI 10.1007/978-0-387-69935-6_1
Book ISBNs
978-0-387-69934-9, 978-0-387-69935-6
Authors

Oded Maimon, Lior Rokach, Maimon, Oded, Rokach, Lior

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 26%
Student > Master 11 21%
Student > Doctoral Student 4 8%
Researcher 3 6%
Student > Bachelor 2 4%
Other 8 15%
Unknown 11 21%
Readers by discipline Count As %
Computer Science 25 47%
Engineering 6 11%
Mathematics 2 4%
Unspecified 2 4%
Agricultural and Biological Sciences 1 2%
Other 5 9%
Unknown 12 23%

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