Authors: Hansen, Pierre
Jaumard, Brigitte
Mladenović, Nenad 
Title: Minimum sum of squares clustering in a low dimensional space
Journal: Journal of Classification
Volume: 15
Issue: 1
First page: 37
Last page: 55
Issue Date: 1-Jan-1998
Rank: M21
ISSN: 0176-4268
DOI: 10.1007/s003579900019
Abstract: 
Clustering with a criterion which minimizes the sum of squared distances to cluster centroids is usually done in a heuristic way. An exact polynomial algorithm, with a complexity in O(N p+1 logN), is proposed for minimum sum of squares hierarchical divisive clustering of points in a p-dimensional space with small p. Empirical complexity is one order of magnitude lower. Data sets with N = 20000 for p = 2, N = 1000 for p = 3, and N = 200 for p = 4 are clustered in a reasonable computing time.
Publisher: Springer Link

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