Combining multiple clusterings using fast simulated annealing

Authors:
Zhiwu Lu;Yuxin Peng;Horace H. S. Ip
Affiliations:
Institute of Computer Science and Technology, Peking University, Beijing 100871, China;Institute of Computer Science and Technology, Peking University, Beijing 100871, China;Department of Computer Science, City University of Hong Kong, Hong Kong
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

This paper presents a fast simulated annealing framework for combining multiple clusterings based on agreement measures between partitions, which are originally used to evaluate a clustering algorithm. Although we can follow a greedy strategy to optimize these measures as the objective functions of clustering ensemble, it may suffer from local convergence and simultaneously incur too large computational cost. To avoid local optima, we consider a simulated annealing optimization scheme that operates through single label changes. Moreover, for the measures between partitions based on the relationship (joined or separated) of pairs of objects, we can update them incrementally for each label change, which ensures that our optimization scheme is computationally feasible. The experimental evaluations demonstrate that the proposed framework can achieve promising results.