# Learning-Augmented k-means Clustering

@article{Ergun2021LearningAugmentedKC, title={Learning-Augmented k-means Clustering}, author={Jon Ergun and Zhili Feng and Sandeep Silwal and David P. Woodruff and Samson Zhou}, journal={ArXiv}, year={2021}, volume={abs/2110.14094} }

k-means clustering is a well-studied problem due to its wide applicability. Unfortunately, there exist strong theoretical limits on the performance of any algorithm for the k-means problem on worst-case inputs. To overcome this barrier, we consider a scenario where “advice” is provided to help perform clustering. Specifically, we consider the k-means problem augmented with a predictor that, given any point, returns its cluster label in an approximately optimal clustering up to some, possibly…

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