On semi-supervised active clustering of stable instances with oracles

Article Type

Research Article

Publication Title

Information Processing Letters


We consider the problem of semi-supervised active clustering under multiplicative perturbation stability with respect to the distance function. Stable instances have an optimal solution that does not change when the distances are perturbed. This captures the notion that the optimal solution is tolerant to measurement errors and uncertainty in the points. Semi-supervision allows us to have an oracle O which answers pairwise queries. We design efficient algorithms to solve problems of multiplicative perturbation stability for semi-supervised clustering by using an ideal as well as a noisy oracle model. We present theoretical performance guarantee of the algorithms.



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