BanditPAM: Almost Linear-Time k-medoids Clustering via Multi-Armed Bandits

TL;DR Want something better than \(k\)-means? Our state-of-the-art \(k\)-medoids algorithm from NeurIPS, BanditPAM, is now publicly available! \(\texttt{pip install banditpam}\) and you're good to go! Like the \(k\)-means problem, the \(k\)-medoids problem is a clustering problem in which our objective is to partition a dataset into disjoint subsets. In \(k\)-medoids, however, we require that the cluster centers must be actual datapoints, which permits greater interpretability of the cluster cen