Mittagsseminar Talk Information

Date and Time: Thursday, January 28, 2010, 12:15 pm

Duration: This information is not available in the database

Location: CAB G51

Speaker: Johannes Blömer (Paderborn University)

Non-uniform sampling and Bregman-clustering on well-separated instances

We consider the k-median problem, i.e. the problem of partitioning a set of n points into k disjoint sets called clusters, such that the sum of the distances of each point to the center of gravity of its cluster is minimized. To measure the distance between points we use so-called Bregman divergences which include among other distance measures the squared Euclidean distance and the Kulback-Leibler divergence from information theory. We show that a non-uniform sampling strategy analogous to the k-means++ method from Arthur and Vassilvitskii computes a constant factor approximation for well-separated instances of the k-median clustering for Bregman divergences.

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