Comiter, MarcusCha, MiriamKung, H.Teerapittayanon, Surat2017-11-282016Comiter, Marcus, Miriam Cha, H. T. Kung, and Surat Teerapittayanon. 2016. "Lambda means clustering: Automatic parameter search and distributed computing implementation." In Proceedings of 23rd International Conference on Pattern Recognition (ICPR), Cancún, Mexico, December 4-8, 2016: 2331-2337.http://nrs.harvard.edu/urn-3:HUL.InstRepos:34390104Recent advances in clustering have shown that ensuring a minimum separation between cluster centroids leads to higher quality clusters compared to those found by methods that explicitly set the number of clusters to be found, such as k-means. One such algorithm is DP-means, which sets a distance parameter λ for the minimum separation. However, without knowing either the true number of clusters or the underlying true distribution, setting λ itself can be difficult, and poor choices in setting λ will negatively impact cluster quality. As a general solution for finding λ, in this paper we present λ-means, a clustering algorithm capable of deriving an optimal value for λ automatically. We contribute both a theoretically-motivated cluster-based version of λ-means, as well as a faster conflict-based version of λ-means. We demonstrate that λ-means discovers the true underlying value of λ asymptotically when run on datasets generated by a Dirichlet Process, and achieves competitive performance on a real world test dataset. Further, we demonstrate that when run on both parallel multicore computers and distributed cluster computers in the cloud, cluster-based λ-means achieves near perfect speedup, and while being a more efficient algorithm, conflict-based λmeans achieves speedups only a factor of two away from the maximum-possible.en-USConference Paper2017-11-2810.1109/ICPR.2016.7899984