M-Estimator induced Fuzzy Clustering Algorithms

DOI

10.2991/eusflat.2011.132How to use a DOI?

Keywords

Fuzzy c-means, M-estimators, Robust statistics, Noise clustering, Multiple prototypes

Abstract

M-estimators can be seen as a special case of robust clustering algorithms. In this paper, we present the reversed direction and show that clustering algorithms can be constructed by using M-estimators. A clever normalization is used to link the values of several M-estimator prototypes together in one clustering algorithm. A variety of M-estimators and several normalization strategies are used in 4 data sets to present their differences and properties. The results are evaluated using 5 different clustering validation indices.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Roland Winkler
AU  - Frank Klawonn
AU  - Rudolf Kruse
PY  - 2011/08
DA  - 2011/08
TI  - M-Estimator induced Fuzzy Clustering Algorithms
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 298
EP  - 304
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.132
DO  - 10.2991/eusflat.2011.132
ID  - Winkler2011/08
ER  -