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...

• Type: Journal
• Year: 2002
• Where: JMLR
• Authors: Alexander Strehl, Joydeep Ghosh
• Keywords: Application Scenarios, Cluster Ensembles, Direct Maximization Approach, JMLR 2002