Multi-Optimisation Consensus Clustering

Authors:
Jian Li;Stephen Swift;Xiaohui Liu
Affiliations:
School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK UB8 3PH;School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK UB8 3PH;School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK UB8 3PH
Venue:
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Year:
2009

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Abstract

Ensemble Clustering has been developed to provide an alternative way of obtaining more stable and accurate clustering results. It aims to avoid the biases of individual clustering algorithms. However, it is still a challenge to develop an efficient and robust method for Ensemble Clustering. Based on an existing ensemble clustering method, Consensus Clustering (CC), this paper introduces an advanced Consensus Clustering algorithm called Multi-Optimisation Consensus Clustering (MOCC), which utilises an optimised Agreement Separation criterion and a Multi-Optimisation framework to improve the performance of CC. Fifteen different data sets are used for evaluating the performance of MOCC. The results reveal that MOCC can generate more accurate clustering results than the original CC algorithm.