Fuzzy clustering ensemble based on mutual information

Authors:
Yan Gao;Shiwen Gu;Liming Xia;Zhining Niao
Affiliations:
Faculty of Information Science and Engineering, Central South University, Hunan, P.R.China;Faculty of Information Science and Engineering, Central South University, Hunan, P.R.China;Faculty of Information Science and Engineering, Central South University, Hunan, P.R.China;Department of Computer Science, Loughborough University, Leics, UK
Venue:
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Year:
2006

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Abstract

Clustering ensemble is a new topic in machine learning. It can find a combined clustering with better quality from multiple partitions. But how to find the combined clustering is a difficult problem. In this paper, we extend the object function proposed by Strehl & Ghosh which is based on mutual information and we present a new algorithm similar to information bottleneck to solve the object function. This algorithm can combine "soft" partitions and need not establish label correspondence between different partitions. We conducted experiments on four real-world data sets to compare our algorithm with other five ensemble algorithms, including CSPA, HGPA, MCLA, QMI. The results indicate that our algorithm provides solutions of improved quality.