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JMLR
2010

Hartigan's Method: k-means Clustering without Voronoi

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Hartigan's Method: k-means Clustering without Voronoi
Hartigan's method for k-means clustering is the following greedy heuristic: select a point, and optimally reassign it. This paper develops two other formulations of the heuristic, one leading to a number of consistency properties, the other showing that the data partition is always quite separated from the induced Voronoi partition. A characterization of the volume of this separation is provided. Empirical tests verify not only good optimization performance relative to Lloyd's method, but also good running time.
Matus Telgarsky, Andrea Vattani
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Matus Telgarsky, Andrea Vattani
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