First, choose k objects n data object as initial cluster centers and for the rest of the other objects, according to their similarity (distance) These cluster centers, respectively assign them to its most similar ( cluster centers represent) clustering and then calculate each cluster center obtained new cluster (the cluster mean all objects) repeats this process until the beginning of the standard measurement function converges. Are generally used as the standard deviation measurement function k clusters has the following characteristics: Each cluster itself as compact as possible, but as much as possible to separate between the clusters. The biggest advantage of this algorithm is simple and fast. The key algorithm is the selection and initial center of the distance formula.

km.m

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