Robust clustering methods: A unified view

Document Type

Article

Publication Date

12-1-1997

Abstract

Clustering methods need to be robust if they are to be useful in practice. In this paper, we analyze several popular robust clustering methods and show that they have much in common. We also establish a connection between fuzzy set theory and robust statistics and point out the similarities between robust clustering methods and statistical methods such as the weighted least-squares (LS) technique, the M estimator, the minimum volume ellipsoid (MVE) algorithm, cooperative robust estimation (CRE), minimization of probability of randomness (MINPRAN), and the epsilon contamination model. By gleaning the common principles upon which the methods proposed in the literature are based, we arrive at a unified view of robust clustering methods. We define several general concepts that are useful in robust clustering, state the robust clustering problem in terms of the defined concepts, and propose generic algorithms and guidelines for clustering noisy data. We also discuss why the generalized Hough transform is a suboptimal solution to the robust clustering problem. © 1997 IEEE.

Identifier

0031139176 (Scopus)

Publication Title

IEEE Transactions on Fuzzy Systems

External Full Text Location

https://doi.org/10.1109/91.580801

ISSN

10636706

First Page

270

Last Page

293

Issue

2

Volume

5

Grant

N00014-96-1-0439

Fund Ref

Office of Naval Research

This document is currently not available here.

Share

COinS