BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits · HackerPapers discussions

BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits — mentioned by eternalban

Wed, 05 Apr 2023 20:32:01 GMT

Thanks for sharing your great work! [Try this tool from arxiv labs (just replace the x in original url with a 5)] https://ar5iv.labs.arxiv.org/html/2006.06856 https://ar5iv.labs.arxiv.org/html/2212.07551

Paper and discussion history. A mention is not an endorsement.

BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits — HN submission

Tue, 04 Apr 2023 20:16:33 GMT

Show HN: Want something better than k-means? Try BanditPAM

Want something better than k-means? I'm happy to announce our SOTA k-medoids algorithm from NeurIPS 2020, BanditPAM, is now publicly available! `pip install banditpam` or `install.packages("banditpam")` and you're good to go! k-means is one of the most widely-used algorithms to cluster data. However, it has several limitations: a) it requires the use of L2 distance for efficient clustering, which also b) restricts the data you're clustering to be vectors, and c) doesn't require the means to be datapoints in the dataset. Unlike in k-means, the k-medoids problem requires cluster centers to be…

281 points; 41 discussion replies on HN. Paper and source history. A submission is not an endorsement.

BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits — mentioned by motiwari

Wed, 29 Mar 2023 19:50:43 GMT

Want something better than k-means? I'm happy to announce our SOTA k-medoids algorithm from NeurIPS 2020, BanditPAM, is now publicly available! `pip install banditpam` or `install.packages("banditpam")` and you're good to go! Unlike in k-means, the k-medoids problem requires cluster centers to be actual datapoints, which permits greater interpretability of your cluster centers. k-medoids also works better with arbitrary distance metrics, so your clustering can be more robust to outliers if you're using metrics like L1. Despite these advantages, most people don't use k-medoids because prior…

Paper and discussion history. A mention is not an endorsement.

BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits — HN submission

Thu, 25 Nov 2021 01:55:55 GMT

BanditPAM, an improved alternative to k-means

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.