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Clustering aggregation by probability accumulation
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DOI：10.1016/j.patcog.2008.09.013.png)
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Abstract
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Since a large number of clustering algorithms exist, aggregating different clustered partitions into a single consolidated one to obtain better results has become an important problem. In Fred and Jain's evidence accumulation algorithm, they construct a co-association matrix on original partition labels, and then apply minimum spanning tree to this matrix for the combined clustering. In this paper, we will propose a novel clustering aggregation scheme, probability accumulation. In this algorithm, the construction of correlation matrices takes the cluster sizes of original clusterings into consideration. An alternate improved algorithm with additional pre- and post-processing is also proposed. Experimental results on both synthetic and real data-sets show that the proposed algorithms perform better than evidence accumulation, as well as some other methods. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Clustering aggregation
Evidence accumulation
Probability accumulation
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