M-Estimator induced Fuzzy Clustering Algorithms
Roland Winkler, Frank Klawonn, Rudolf Kruse
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.132How to use a DOI?
- Fuzzy c-means, M-estimators, Robust statistics, Noise clustering, Multiple prototypes
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 298 EP - 304 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2011.132 DO - https://doi.org/10.2991/eusflat.2011.132 ID - Winkler2011/08 ER -