Local Search for Clustering in Almost-Linear Time

This talk presents the first local search algorithm for Euclidean clustering that achieves an O(1)-approximation in almost-linear time. It develops a new one-swap local search framework and an efficient approximate nearest-neighbor data structure, with applications to k-means and other metric clustering settings.

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Seminar discussion and exchangeProf. Yaonan Jin presenting the seminar