Scalable K-Means by ranked retrieval

WSDM, 2014.

Cited by: 39|Bibtex|Views108|DOI:https://doi.org/10.1145/2556195.2556260
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Other Links: dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

The k-means clustering algorithm has a long history and a proven practical performance, however it does not scale to clustering millions of data points into thousands of clusters in high dimensional spaces. The main computational bottleneck is the need to recompute the nearest centroid for every data point at every iteration, aprohibitive...More

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