The fuzzy mega-cluster: Robustifying FCM by scaling down memberships

Document Type

Conference Proceeding

Publication Date

1-1-2006

Abstract

A new robust clustering scheme based on fuzzy c-means is proposed and the concept of a fuzzy mega-cluster is introduced in this paper. The fuzzy mega-cluster is conceptually similar to the noise cluster, designed to group outliers in a separate cluster. This proposed scheme, called the mega-clustering algorithm is shown to be robust against outliers. Another interesting property is its ability to distinguish between true outliers and non-outliers (vectors that are neither part of any particular cluster nor can be considered true noise). Robustness is achieved by scaling down the fuzzy memberships, as generated by FCM so that the infamous unity constraint of FCM is relaxed with the intensity of scaling differing across datum. The mega-clustering algorithm is tested on noisy data sets from literature and the results presented. © Springer-Verlag Berlin Heidelberg 2005.

Identifier

33748996199 (Scopus)

ISBN

[9783540283126]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/11539506_57

e-ISSN

16113349

ISSN

03029743

First Page

444

Last Page

453

Volume

3613 LNAI

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