research-article

Unsupervised Optimal Fuzzy Clustering

Authors:

A. B. GevAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 11, Issue 7

Pages 773 - 780

https://doi.org/10.1109/34.192473

Published: 01 July 1989 Publication History

Abstract

This study reports on a method for carrying out fuzzy classification without a priori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolume and density criteria. An algorithm is derived from a combination of the fuzzy K-means algorithm and fuzzy maximum-likelihood estimation. The unsupervised fuzzy partition-optimal number of classes algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster. The algorithm was tested on different classes of simulated data, and on a real data set derived from sleep EEG signal.

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Index Terms

Unsupervised Optimal Fuzzy Clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 11, Issue 7

July 1989

109 pages

ISSN:0162-8828

Editor:
S. L. Tanimoto
Univ. of Washington, Seattle

Issue’s Table of Contents

Copyright © Copyright © 1989 IEEE. All Rights Reserved.

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 July 1989

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