Abstract
Clique partitioning in Euclidean space R n consists in finding a partition of a given set of N points into M clusters in order to minimize the sum of within-cluster interpoint distances. For n=1 clusters need not consist of consecutive points on a line but have a nestedness property. Exploiting this property, an O(N 5M 2) dynamic programming algorithm is proposed. A θ(N) algorithm is also given for the case M=2.
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