In k-means clustering we are given a set of n data points in d-dimensional space and an integer k, and the problem is to determine a set of k points in d-space, called centers, so as to minimize the mean squared distance from each data point to its neares
By Oleg Krivtsov - Implementing Lucas-Kanade and Baker-Dellaert-Matthews image alignment algorithms (remember to check l8r freaking site changes the urls)