BanditPAM: Almost Linear-Time k-medoids Clustering via Multi-Armed Bandits
Analysis
This article announces the public release of BanditPAM, a new k-medoids clustering algorithm developed at Stanford AI. The key advantage of BanditPAM is its speed, achieving O(n log n) complexity compared to the O(n^2) of previous algorithms. This makes k-medoids, which offers benefits like interpretable cluster centers and robustness to outliers, more practical for large datasets. The article highlights the ease of use, with a simple pip install and an interface similar to scikit-learn's KMeans. The availability of a video summary, PyPI package, GitHub repository, and full paper further enhances accessibility and encourages adoption by ML practitioners. The comparison to k-means is helpful for understanding the context and motivation behind the work.
Key Takeaways
- •BanditPAM is a new, faster k-medoids clustering algorithm.
- •It offers improved speed (O(n log n)) compared to previous k-medoids algorithms.
- •It's easy to install and use, with a scikit-learn-like interface.
“In k-medoids, however, we require that the cluster centers must be actual datapoints, which permits greater interpretability of the cluster centers.”