A Decision Model for Fuzzy Clustering Ensemble

DOI

10.2991/iske.2007.210How to use a DOI?

Keywords

Fuzzy Clustering Ensemble,

Abstract

Recent research and experiments have showed that clustering ensemble approaches can enhance the robustness and stabilities of unsupervised learning greatly. Most of them focused on crisp clustering combination. However, in this paper, we offer a decision model based on fuzzy set theory for fuzzy clustering ensemble. Firstly, obtain the optimal partition called “expert” from H individual fuzzy partitions generated by fuzzy c-means algorithm. Then, use fuzzy voting scheme to generate the majority judger. Finally, the two matrixes are combined by Decision Model. Experimental results show the effectiveness of the proposed method comparing to the results based on crisp clustering.

Copyright

© 2007, 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  - Yanqiu Fu
AU  - Yan Yang
PY  - 2007/10
DA  - 2007/10
TI  - A Decision Model for Fuzzy Clustering Ensemble
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 1236
EP  - 1241
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.210
DO  - 10.2991/iske.2007.210
ID  - Fu2007/10
ER  -