Similarity Based Fuzzy and Possibilistic c-means Algorithm
Chunhui Zhang1, Yiming Zhou, Trevor Martin
1School of Computer Science and Technology, Beihang Univ.
Available Online December 2008.
- 10.2991/jcis.2008.9How to use a DOI?
- similarity, fuzzy clustering
A similarity based fuzzy and possibilistic c-means algorithm called SFPCM is presented in this paper. It is derived from original fuzzy and possibilistic c-means algorithm(FPCM) which was proposed by Bezdek. The difference between the two algorithms is that the proposed SFPCM algorithm processes relational data, and the original FPCM algorithm processes propositional data. Experiments are performed on 22 data sets from the UCI repository to compare SFPCM with FPCM. The results show that these two algorithms can generate similar results on the same data sets. SFPCM performs a little better than FPCM in the sense of classification accuracy, and it also converges more quickly than FPCM on these data sets.
- © 2008, 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 - Chunhui Zhang AU - Yiming Zhou AU - Trevor Martin PY - 2008/12 DA - 2008/12 TI - Similarity Based Fuzzy and Possibilistic c-means Algorithm BT - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008) PB - Atlantis Press SP - 54 EP - 59 SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2008.9 DO - 10.2991/jcis.2008.9 ID - Zhang2008/12 ER -