k -center Clustering under Perturbation Resilience
نویسندگان
چکیده
منابع مشابه
k-Center Clustering Under Perturbation Resilience
The k-center problem is a canonical and long-studied facility location and clustering problem with many applications in both its symmetric and asymmetric forms. Both versions of the problem have tight approximation factors on worst case instances: a 2-approximation for symmetric kcenter and an O(log*(k))-approximation for the asymmetric version. Therefore to improve on these ratios, one must go...
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Motivated by the fact that distances between data points in many real-world clustering instances are often based on heuristic measures, Bilu and Linial [6] proposed analyzing objective based clustering problems under the assumption that the optimum clustering to the objective is preserved under small multiplicative perturbations to distances between points. In this paper, we provide several res...
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Clustering under most popular objective functions is NP-hard, even to approximate well, and so unlikely to be efficiently solvable in the worst case. Recently, Bilu and Linial [11] suggested an approach aimed at bypassing this computational barrier by using properties of instances one might hope to hold in practice. In particular, they argue that instances in practice should be stable to small ...
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The k-center problem is a canonical and long-studied facility location and clustering problem with many applications in both its symmetric and asymmetric forms. Both versions of the problem have tight approximation factors on worst case instances: a 2-approximation for symmetric k-center and an O(log∗(k))-approximation for the asymmetric version. Therefore to improve on these ratios, one must g...
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Recently, Bilu and Linial [6] formalized an implicit assumption often made when choosing a clustering objective: that the optimum clustering to the objective should be preserved under small multiplicative perturbations to distances between points. Balcan and Liang [4] generalized this to a relaxed notion where the optimal clustering after perturbation is allowed to change slightly. In this pape...
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ژورنال
عنوان ژورنال: ACM Transactions on Algorithms
سال: 2020
ISSN: 1549-6325,1549-6333
DOI: 10.1145/3381424