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Makingk-means even faster
[chapter]
2010
Proceedings of the 2010 SIAM International Conference on Data Mining
The k-means algorithm is widely used for clustering, compressing, and summarizing vector data. In this paper, we propose a new acceleration for exact k-means that gives the same answer, but is much faster in practice. Like Elkan's accelerated algorithm [8], our algorithm avoids distance computations using distance bounds and the triangle inequality. Our algorithm uses one novel lower bound for point-center distances, which allows it to eliminate the innermost k-means loop 80% of the time or
doi:10.1137/1.9781611972801.12
dblp:conf/sdm/Hamerly10
fatcat:5d3ulgfynfb2bgvorezkm6szw4