Robust fuzzy clustering algorithms

Document Type

Conference Proceeding

Publication Date

1-1-1993

Abstract

A class of fuzzy clustering algorithms based on a recently introduced 'noise cluster' concepts is proposed. A 'noise prototype' is defined such that it is equi-distant to all the points in the data-set. This allows for detection of clusters amongst data with or without noise. It is shown that this concept is applicable to all the generalizations of fuzzy or hard k-means algorithms. Various applications are also considered. Application of this concept to a variety of regression problems is also considered. It is shown that the results of this approach are comparable to many robust regression techniques. The paper concludes with a summary and directions for future work.

Identifier

0027221185 (Scopus)

ISBN

[0780306155]

Publication Title

1993 IEEE International Conference on Fuzzy Systems

First Page

1281

Last Page

1286

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