An efficient Split-Merge re-start for the K-means algorithm
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Jain, 1988, Algorithms for Clustering Data
Kaufman, 1987, Clustering by Means of Medoids
Bottou, Convergence properties of the K-means algorithms, Proc. Advances Neural Inf. Process. Syst., 585
Elkan, Using the triangle inequality to accelerate k-means, Proc. 20th Int. Conf. Mach. Learn., 147
Ding, Yinyang K-means: A drop-in replacement of the classic k-means with consistent speedup, Proc. Int. Conf. Mach. Learn., 579
Balcan, Distributed $ k$k-means and $ k$k-median clustering on general topologies, Proc. Advances Neural Inf. Process. Syst., 1995
Forgy, 1965, Cluster analysis of multivariate data: Efficiency versus interpretability of classifications, Biometrics, 21, 768
Arthur, K-means++: The advantages of careful seeding, Proc. 18th Annu. ACM-SIAM Symp. Discrete Algorithms, 1027
Nielsen, 2014, Further heuristics for $ k$k-means: The merge-and-split heuristic and the $(k, l)$(k,l)-means
Telgarsky, Hartigan’s method: K-means clustering without voronoi, Proc. 13th Int. Conf. Artif. Intell. Statist., 820
Slonim, Hartigan’s K-means versus lloyd’s K-means-is it time for a change?, Proc. 23rd Int. Joint Conf. Artif. Intell., 1677
Pelleg, X-means: Extending K-means with efficient estimation of the number of clusters, Proc. 17th Int. Conf. Mach. Learn., 727
Ball, 1965, Isodata, a novel method of data analysis and pattern classification
Bachem, Fast and provably good seedings for k-means, Proc. Advances Neural Inf. Process. Syst., 55