|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.
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