Abstract
A problem of partitioning a finite sequence of points in Euclidean space into two subsequences (clusters) maximizing the size of the first cluster subject to two constraints is considered. The first constraint deals with every two consecutive indices of elements of the first cluster: the difference between them is bounded from above and below by some constants. The second one restricts the value of a quadratic clustering function that is the sum of the intracluster sums over both clusters. The intracluster sum is the sum of squared distances between cluster elements and the cluster center. The center of the first cluster is unknown and determined as the centroid (i.e. as the mean value of its elements), while the center of the second one is zero. The strong NP-hardness of the problem is shown and an exact algorithm is suggested for the case of integer coordinates of input points. If the space dimension is bounded by some constant this algorithm runs in a pseudopolynomial time.
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