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ICML
2007
IEEE

Best of both: a hybridized centroid-medoid clustering heuristic

10 years 5 days ago
Best of both: a hybridized centroid-medoid clustering heuristic
Although each iteration of the popular kMeans clustering heuristic scales well to larger problem sizes, it often requires an unacceptably-high number of iterations to converge to a solution. This paper introduces an enhancement of k-Means in which local search is used to accelerate convergence without greatly increasing the average computational cost of the iterations. The local search involves a carefully-controlled number of swap operations resembling those of the more robust k-Medoids clustering heuristic. We show empirically that the proposed method improves convergence results when compared to standard k-Means.
Nizar Grira, Michael E. Houle
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Nizar Grira, Michael E. Houle
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