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K-means algorithm
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KMEANS uses a two-phase iterative algorithm to minimize the sum of point-to-centroid distances, summed over all K clusters. The first phase uses what the literature often describes as "batch" updates, where each iteration consists of reassigning points to their nearest cluster centroid, all at once, followed by recalculation of cluster centroids. This phase may be thought of as providing a fast but potentially only approximate solution as a starting point for the second phase. The second phase uses what the literature often describes as "on-line" updates, where points are individually reassigned if doing so will reduce the sum of distances, and cluster centroids are recomputed after each reassignment. Each iteration during this second phase consists of one pass though all the points. KMEANS can converge to a local optimum, which in this case is a partition of points in which moving any single point to a different cluster increases the total sum
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