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Semi-Supervised Algorithms for Approximately Optimal and Accurate Clustering
2018
International Colloquium on Automata, Languages and Programming
We study k-means clustering in a semi-supervised setting. Given an oracle that returns whether two given points belong to the same cluster in a fixed optimal clustering, we investigate the following question: how many oracle queries are sufficient to efficiently recover a clustering that, with probability at least (1 − δ), simultaneously has a cost of at most (1 + ) times the optimal cost and an accuracy of at least (1 − )? We show how to achieve such a clustering on n points with O((k ) oracle
doi:10.4230/lipics.icalp.2018.57
dblp:conf/icalp/GamlathHS18
fatcat:yme5qmwocjb2focyhtnfyozzq4