Multiobjective data clustering

Conventional clustering algorithms utilize a single criterion that may not conform to the diverse shapes of the underlying clusters. We offer a new clustering approach that uses multiple clustering objective functions simultaneously. The proposed multiobjective clustering is a two-step process. It i...

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Hauptverfasser: Law, M.H.C., Topchy, A.P., Jain, A.K.
Format: Tagungsbericht
Sprache:eng
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Beschreibung
Zusammenfassung:Conventional clustering algorithms utilize a single criterion that may not conform to the diverse shapes of the underlying clusters. We offer a new clustering approach that uses multiple clustering objective functions simultaneously. The proposed multiobjective clustering is a two-step process. It includes detection of clusters by a set of candidate objective functions as well as their integration into the target partition. A key ingredient of the approach is a cluster goodness junction that evaluates the utility of multiple clusters using re-sampling techniques. Multiobjective data clustering is obtained as a solution to a discrete optimization problem in the space of clusters. At meta-level, our algorithm incorporates conflict resolution techniques along with the natural data constraints. An empirical study on a number of artificial and real-world data sets demonstrates that multiobjective data clustering leads to valid and robust data partitions.
ISSN:1063-6919
DOI:10.1109/CVPR.2004.1315194