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Soft Computing for Data Mining Applications

Overview of attention for book
Cover of 'Soft Computing for Data Mining Applications'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction
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    Chapter 2 Self Adaptive Genetic Algorithms
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    Chapter 3 Characteristic Amplification Based Genetic Algorithms
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    Chapter 4 Dynamic Association Rule Mining Using Genetic Algorithms
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    Chapter 5 Evolutionary Approach for XML Data Mining
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    Chapter 6 Soft Computing Based CBIR System
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    Chapter 7 Fuzzy Based Neuro - Genetic Algorithm for Stock Market Prediction
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    Chapter 8 Data Mining Based Query Processing Using Rough Sets and GAs
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    Chapter 9 Hashing the Web for Better Reorganization
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    Chapter 10 Algorithms for Web Personalization
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    Chapter 11 Classifying Clustered Webpages for Effective Personalization
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    Chapter 12 Mining Top - k Ranked Webpages Using SA and GA
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    Chapter 13 A Semantic Approach for Mining Biological Databases
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    Chapter 14 Probabilistic Approach for DNA Compression
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    Chapter 15 Non-repetitive DNA Compression Using Memoization
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    Chapter 16 Exploring Structurally Similar Protein Sequence Motifs
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    Chapter 17 Matching Techniques in Genomic Sequences for Motif Searching
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    Chapter 18 Merge Based Genetic Algorithm for Motif Discovery
Attention for Chapter 7: Fuzzy Based Neuro - Genetic Algorithm for Stock Market Prediction
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Citations

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Chapter title
Fuzzy Based Neuro - Genetic Algorithm for Stock Market Prediction
Chapter number 7
Book title
Soft Computing for Data Mining Applications
Published by
Springer, Berlin, Heidelberg, January 2009
DOI 10.1007/978-3-642-00193-2_7
Book ISBNs
978-3-64-200192-5, 978-3-64-200193-2
Authors

K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 40%
Student > Master 2 20%
Student > Bachelor 1 10%
Unknown 3 30%
Readers by discipline Count As %
Computer Science 5 50%
Economics, Econometrics and Finance 1 10%
Engineering 1 10%
Unknown 3 30%

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