Want something better than k-means? Try BanditPAM

In plain words

BanditPAM is a k-medoids clustering algorithm available for Python and R that improves upon traditional k-means clustering. Unlike k-means, it works with any distance metric and requires cluster centers to be actual data points, offering better interpretability and robustness to outliers. It is designed for researchers and data scientists who need flexible clustering across various data types beyond vectors.

written from the facts on this page · September 2026

From the sources

BanditPAM C++ implementation and Python package. Contribute to motiwari/BanditPAM development by creating an account on GitHub.

In the maker’s words, at launch

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…

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