Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions

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Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions (2002)

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by Alexander Strehl , Joydeep Ghosh , Claire Cardie

Venue:	Journal of Machine Learning Research
Citations:	601 - 20 self

BibTeX

@ARTICLE{Strehl02clusterensembles,
    author = {Alexander Strehl and Joydeep Ghosh and Claire Cardie},
    title = {Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions},
    journal = {Journal of Machine Learning Research},
    year = {2002},
    volume = {3},
    pages = {583--617}
}

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Abstract

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 to use a supra-consensus function that evaluates all three approaches against the objective function and picks the best solution for a given situation. We evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution. Promising results are obtained in all three situations for synthetic as well as real data-sets.

Keyphrases

cluster ensemble knowledge reuse framework multiple partition non-identical set cluster ensemble knowledge reuse main purpose high-quality combiners first combiner cluster ensemble problem original cluster several application scenario real data-sets objective function common data-set different application scenario combinatorial optimization problem hypergraph partitioning original clustering algorithm low computational cost second combiner supra-consensus function multiple clustering direct maximization approach combined clustering mutual information similarity measure multiple partitioning consensus function efficient technique promising result

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