Streaming PTAS for Constrained k-Means
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We generalise the results of Bhattacharya et al. (Journal of Computing Systems, 62(1):93-115, 2018) for the list-k-means problem defined as – for a (unknown) partition X_1, ..., X_k of the dataset X ⊆R^d, find a list of k-center sets (each element in the list is a set of k centers) such that at least one of k-center sets {c_1, ..., c_k} in the list gives an (1+ε)-approximation with respect to the cost function min_permutation π[ ∑_i=1^k∑_x ∈ X_i ||x - c_π(i)||^2 ]. The list-k-means problem is important for the constrained k-means problem since algorithms for the former can be converted to PTAS for various versions of the latter. Following are the consequences of our generalisations: - Streaming algorithm: Our D^2-sampling based algorithm running in a single iteration allows us to design a 2-pass, logspace streaming algorithm for the list-k-means problem. This can be converted to a 4-pass, logspace streaming PTAS for various constrained versions of the k-means problem. To the best of our knowledge, these are the first constant pass, logspace streaming PTASs for constrained versions of the k-means problem. - Faster PTAS under stability: Our generalisation is also useful in k-means clustering scenarios where finding good centers becomes easy once good centers for a few "bad" clusters have been chosen. One such scenario is clustering under stability where the number of such bad clusters is a constant. Using the above idea, we significantly improve the running time of the known algorithm from O(dn^3) (k logn)^poly(1/β, 1/ε) to O (dn^3 k^Õ_βε(1/βε)).
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