On the selection of m for Fuzzy c-Means

DOI

10.2991/ifsa-eusflat-15.2015.224How to use a DOI?

Keywords

Fuzzy clustering, Fuzzy c-means, parameters of FCM, m.

Abstract

Fuzzy c-means is a well known fuzzy clustering algorithm. It is an unsupervised clustering algorithm that permits us to build a fuzzy partition from data. The algorithm depends on a parameter m which corresponds to the degree of fuzziness of the solution. Large values of m will blur the classes and all elements tend to belong to all clusters. The solutions of the optimization problem depend on the parameter m. That is, different selections of m will typically lead to different partitions. In this paper we study and compare the effect of the selection of m obtained from the fuzzy c-means.

Copyright

© 2015, 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  - Vicenç Torra
PY  - 2015/06
DA  - 2015/06
TI  - On the selection of m for Fuzzy c-Means
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 1571
EP  - 1577
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.224
DO  - 10.2991/ifsa-eusflat-15.2015.224
ID  - Torra2015/06
ER  -