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Soft Computing for Data Mining Applications

Overview of attention for book
Soft Computing for Data Mining Applications
Springer Berlin Heidelberg

Table of Contents

  1. Altmetric Badge
    Book Overview
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    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 11: Classifying Clustered Webpages for Effective Personalization
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Chapter title
Classifying Clustered Webpages for Effective Personalization
Chapter number 11
Book title
Soft Computing for Data Mining Applications
Published by
Springer, Berlin, Heidelberg, January 2009
DOI 10.1007/978-3-642-00193-2_11
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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