K Means Cluster Platform Overview
K Means Cluster is one of four platforms that JMP provides for clustering observations. For a comparison of all four methods, see “Overview of Platforms for Clustering Observations”.
The K Means Cluster platform forms a specified number of clusters using an iterative fitting process. The k-means algorithm first selects a set of n points called cluster seeds as an initial guess for the means of the clusters. Each observation is assigned to the nearest cluster seed to form a set of temporary clusters. The seeds are then replaced by the cluster means, the points are reassigned, and the process continues until no further changes occur in the clusters.
The k-means algorithm is a special case of the EM algorithm ...

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