On the selection of m for Fuzzy c-Means
- https://doi.org/10.2991/ifsa-eusflat-15.2015.224How to use a DOI?
- Fuzzy clustering, Fuzzy c-means, parameters of FCM, m.
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/ifsa-eusflat-15.2015.224 ID - Torra2015/06 ER -