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Combining multiple clusterings using similarity graph
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DOI：10.1016/j.patcog.2010.09.008.png)
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Abstract
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Multiple clusterings are produced for various needs and reasons in both distributed and local environments. Combining multiple clusterings into a final clustering which has better overall quality has gained importance recently. It is also expected that the final clustering is novel, robust, and scalable. In order to solve this challenging problem we introduce a new graph-based method. Our method uses the evidence accumulated in the previously obtained clusterings, and produces a very good quality final clustering. The number of clusters in the final clustering is obtained automatically; this is another important advantage of our technique. Experimental test results on real and synthetically generated data sets demonstrate the effectiveness of our new method. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Clustering
Combining clustering partitions
Cluster ensemble
Evidence accumulation
Robust clustering
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