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Soft Computing for Data Mining Applications

  • Book
  • © 2009

Overview

Authors:
  1. K. R. Venugopal

    1. Bangalore University, India

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  2. K. G. Srinivasa

    1. M.S. Ramaiah Institute of Technology, India

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  3. L. M. Patnaik

    1. Defence Institute of Advanced Technology, Pune, India

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  • Recent research in the fields of Data Mining in combination with Soft Computing methodologies
  • State-of-the-art technology in data mining
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 190)

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Table of contents (18 chapters)

  1. Front Matter

    Download chapter PDF

  2. Introduction

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 1-17

  3. Self Adaptive Genetic Algorithms

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 19-50

  4. Characteristic Amplification Based Genetic Algorithms

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 51-62

  5. Dynamic Association Rule Mining Using Genetic Algorithms

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 63-80

  6. Evolutionary Approach for XML Data Mining

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 81-118

  7. Soft Computing Based CBIR System

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 119-137

  8. Fuzzy Based Neuro - Genetic Algorithm for Stock Market Prediction

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 139-166

  9. Data Mining Based Query Processing Using Rough Sets and GAs

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 167-195

  10. Hashing the Web for Better Reorganization

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 197-215

  11. Algorithms for Web Personalization

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 217-230

  12. Classifying Clustered Webpages for Effective Personalization

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 231-247

  13. Mining Top - k Ranked Webpages Using SA and GA

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 249-258

  14. A Semantic Approach for Mining Biological Databases

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 259-278

  15. Probabilistic Approach for DNA Compression

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 279-289

  16. Non-repetitive DNA Compression Using Memoization

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 291-301

  17. Exploring Structurally Similar Protein Sequence Motifs

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 303-318

  18. Matching Techniques in Genomic Sequences for Motif Searching

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 319-330

  19. Merge Based Genetic Algorithm for Motif Discovery

    • K. R. Venugopal, K. G. Srinivasa, L. M. Patnaik
    Pages 331-341
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Keywords

About this book

The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the ?elds of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. Asaresult,traditionaladhocmixturesofstatisticaltechniquesanddata managementtools are no longer adequate for analyzing this vast collection of data. Severaldomainswherelargevolumesofdataarestoredincentralizedor distributeddatabasesincludesapplicationslikeinelectroniccommerce,bio- formatics, computer security, Web intelligence, intelligent learning database systems,?nance,marketing,healthcare,telecommunications,andother?elds. E?cient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the ca- bility of computers to search huge amounts of data in a fast and e?ective manner. However,the data to be analyzed is imprecise and a?icted with - certainty. In the case of heterogeneous data sources such as text and video, the data might moreover be ambiguous and partly con?icting. Besides, p- terns and relationships of interest are usually approximate. Thus, in order to make the information mining process more robust it requires tolerance toward imprecision, uncertainty and exceptions.

Authors and Affiliations

  • Bangalore University, India

    K. R. Venugopal

  • M.S. Ramaiah Institute of Technology, India

    K. G. Srinivasa

  • Defence Institute of Advanced Technology, Pune, India

    L. M. Patnaik

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