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JMLR
2002

Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions

13 years 3 months ago
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions
This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant `knowledge reuse' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners (consensus functions). The first combiner induces a similarity measure from the partitionings and then reclusters the objects. The second combiner is based on hypergraph partitioning. The third one collapses groups of clusters into meta-clusters which then compete for each object to determine the combined clustering. Due to the low computational costs of our techniques, it is quite feasible t...
Alexander Strehl, Joydeep Ghosh
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where JMLR
Authors Alexander Strehl, Joydeep Ghosh
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