A statistical method that creates optimally separated groups of Observations in data using one of several methods. A set of points called cluster seeds is selected as a first guess of the means of the clusters. One cluster seed is selected for each of k clusters. Each observation is assigned to the nearest seed to form temporary clusters. The seeds are then replaced by the Means of the temporary clusters, and the process is repeated until no further changes occur in the clusters.