Soft Computing for Data Mining Applications

Soft Computing for Data Mining Applications

Bibliographic Details
Main Authors: Venugopal, K. R. (Author), Srinivasa, K.G. (Author), Patnaik, L. M. (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Edition:1st ed. 2009.
Series:Studies in Computational Intelligence, 190
Subjects:
Computer-aided engineering.
Applied mathematics.
Engineering mathematics.
Artificial intelligence.
Computer-Aided Engineering (CAD, CAE) and Design.
Mathematical and Computational Engineering.
Artificial Intelligence.
Online Access:https://doi.org/10.1007/978-3-642-00193-2
Table of Contents:
  • Self Adaptive Genetic Algorithms
  • Characteristic Amplification Based Genetic Algorithms
  • Dynamic Association Rule Mining Using Genetic Algorithms
  • Evolutionary Approach for XML Data Mining
  • Soft Computing Based CBIR System
  • Fuzzy Based Neuro - Genetic Algorithm for Stock Market Prediction
  • Data Mining Based Query Processing Using Rough Sets and GAs
  • Hashing the Web for Better Reorganization
  • Algorithms for Web Personalization
  • Classifying Clustered Webpages for Effective Personalization
  • Mining Top - k Ranked Webpages Using SA and GA
  • A Semantic Approach for Mining Biological Databases
  • Probabilistic Approach for DNA Compression
  • Non-repetitive DNA Compression Using Memoization
  • Exploring Structurally Similar Protein Sequence Motifs
  • Matching Techniques in Genomic Sequences for Motif Searching
  • Merge Based Genetic Algorithm for Motif Discovery.

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