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Found 2 papers, 2 papers with code
BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits
Current state-of-the-art $k$-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size $n$ for each iteration, being prohibitively expensive for large datasets.
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits
In these experiments, we observe that BanditPAM returns the same results as state-of-the-art PAM-like algorithms up to 4x faster while performing up to 200x fewer distance computations.
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