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tanaylab
tglkmeans:Efficient Implementation of K-Means++ Algorithm
Efficient implementation of K-Means++ algorithm. For more information see (1) "kmeans++ the advantages of the k-means++ algorithm" by David Arthur and Sergei Vassilvitskii (2007), Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman and Chaitanya Swamy <doi:10.1145/2395116.2395117>.
Maintained by Aviezer Lifshitz. Last updated 2 months ago.
algorithms-implementedkmeanscpp
22.4 match 7 stars 5.35 score 16 scriptstanaylab
tgstat:Amos Tanay's Group High Performance Statistical Utilities
A collection of high performance utilities to compute distance, correlation, auto correlation, clustering and other tasks. Contains graph clustering algorithm described in "MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions" (Yael Baran, Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi, Elad Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz & Amos Tanay, 2019 <doi:10.1186/s13059-019-1812-2>).
Maintained by Aviezer Lifshitz. Last updated 6 months ago.
algorithms-implementedcorrelationknnstatisticsopenblascpp
15.5 match 8 stars 6.06 score 24 scripts 1 dependents