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MLMTA
2007

Consensus Based Ensembles of Soft Clusterings

13 years 5 months ago
Consensus Based Ensembles of Soft Clusterings
— Cluster Ensembles is a framework for combining multiple partitionings obtained from separate clustering runs into a final consensus clustering. This framework has attracted much interest recently because of its numerous practical applications, and a variety of approaches including Graph Partitioning, Maximum Likelihood, Genetic algorithms, and Voting-Merging have been proposed. The vast majority of these approaches accept hard clusterings as input. There are, however, many clustering algorithms such as EM and fuzzy c-means that naturally output soft partitionings of data, and forcibly hardening these partitions before obtaining a consensus potentially involves loss of valuable information. In this paper we propose several consensus algorithms that work on soft clusterings and experiment with many real-life datasets to empirically show that using soft clusterings as input does offer significant advantages, especially when dealing with vertically partitioned data.
Kunal Punera, Joydeep Ghosh
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where MLMTA
Authors Kunal Punera, Joydeep Ghosh
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