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Cluster Analysis for Data Mining and System Identification

  • Book
  • © 2007

Overview

Authors:
  1. János Abonyi

    1. Department of Process Engineering, University of Pannonia, Hungary

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    You can also search for this author in PubMed   Google Scholar

  2. Balázs Feil

    1. Department of Process Engineering, University of Pannonia, Hungary

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    You can also search for this author in PubMed   Google Scholar

  • Detailed overview of the most powerful algortihms and approaches for data mining and system identification is presented
  • Extensive references give a good overview of the current state of the application of computational intelligence in data mining and system identification, and suggest further reading for additional research
  • Numerous illustrations to facilitate the understanding of ideas and methods presented
  • Supporting MATLAB files, available at the website www.fmt.uni-pannon.hu/softcomp create a computational platform for exploration and illustration of many concepts and algorithms presented in the book
  • Includes supplementary material: sn.pub/extras

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Classical Fuzzy Cluster Analysis

    Pages 1-45

  3. Visualization of the Clustering Results

    Pages 47-80

  4. Clustering for Fuzzy Model Identification — Regression

    Pages 81-140

  5. Fuzzy Clustering for System Identification

    Pages 141-224

  6. Fuzzy Model based Classifiers

    Pages 225-252

  7. Segmentation of Multivariate Time-series

    Pages 253-273

  8. Back Matter

    Pages 275-303
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Keywords

About this book

Dataclusteringisacommontechniqueforstatisticaldataanalysis,whichisusedin many ?elds, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the classi?cation of similar objects into di?erent groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait – often proximity according to some de?ned distance measure. The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data, but it can be used for visuali- tion,regression,classi?cationandtime-seriesanalysis,hence fuzzy cluster analysis is a good approach to solve complex data mining and system identi?cation pr- lems. Overview In the last decade the amount of the stored data has rapidly increased related to almost all areas of life. The most recent survey was given by Berkeley University of California about the amount of data. According to that, data produced in 2002 and stored in pressed media, ?lms and electronics devices only are about 5 - abytes. For comparison, if all the 17 million volumes of Library of Congress of the UnitedStatesofAmericaweredigitalized,itwouldbeabout136terabytes. Hence, 5 exabytes is about 37,000 Library of Congress. If this data mass is projected into 6. 3 billion inhabitants of the Earth, then it roughly means that each contem- rary generates 800 megabytes of data every year. It is interesting to compare this amount with Shakespeare’s life-work, which can be stored even in 5 megabytes.

Authors and Affiliations

  • Department of Process Engineering, University of Pannonia, Hungary

    János Abonyi, Balázs Feil

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