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Abstract: We consider the problem of partitioning a set of m points in the n-dimensional Euclidean space into k clusters (usually m and n are variable, while k is fixed), so as to minimize the sum of squared distances between each point and its cluster center. This formulation is usually the objective of the k-means clustering algorithm (Kanungo et al. (2000)). We prove that this problem in NP-hard even for k 2, and we consider a continuous relaxation of this discrete problem: find the k-dimensional... (Update)
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BibTeX entry: (Update)
@misc{ graphs-machine,
author = "Clustering Large Graphs",
title = "Machine Learning, 56, 9--33, 2004",
url = "citeseer.ist.psu.edu/751063.html" }
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