Bandit-PAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits

Tiwari Mo
Tiwari Mo
Zhang Martin Jinye
Zhang Martin Jinye
Mayclin James
Mayclin James
Thrun Sebastian
Thrun Sebastian
Cited by: 0|Bibtex|Views2
Other Links: arxiv.org

Abstract:

Clustering is a ubiquitous task in data science. Compared to the commonly used $k$-means clustering algorithm, $k$-medoids clustering algorithms require the cluster centers to be actual data points and support arbitrary distance metrics, allowing for greater interpretability and the clustering of structured objects. Current state-of-the...More

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