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Lloyd's method is identical to the k-means algorithm. Oddly, the k-means article says the method "converges very quickly in practice", while this one says "the algorithm converges slowly". — Jeﬀ Erickson✍ 04:30, 6 April 2007 (UTC)
The principal difference between Lloyd's method and the k-means algorithm is that k-means applies to a finite set of prescribed discrete entities, whereas the method described on this page applies to a continuum with a prescribed density function. The 'points' referred to on this page correspond to the 'centroids' of k-means clusters, in each case iteratively optimised to model the distribution of the prescribed data.
Mathematically these are indeed equivalent. Computationally, however, they are very different - the difference between a discrete and a continuous problem. This explains the difference in performance, and the fact that of the two methods only k-means can guarantee convergence in finitely many iterations. - Robert Stanforth 14:50, 15 May 2007 (UTC)
I think the two are different enough to remove the merge tag. Weston.pace 19:50, 12 June 2007 (UTC)
LLoyd's algorithm is the most popular method to find an approximate solution to the k-means problem. --22.214.171.124 (talk) 06:26, 13 October 2011 (UTC)