Pattern recognition clustering analysis. K-means clustering algorithm is to randomly K objects as the initial cluster centers. Then calculate the distance of each object and each seed cluster centers, assigning each object to its distance the nearest cluster center. Cluster centers and the object assigned to them on behalf of a cluster. Once all the objects are assigned, the cluster centers of each cluster will be recalculated based on the existing cluster object. This process is repeated until a termination condition is satisfied. Termination condition may not be (or smallest numbers) is reassigned to a different target cluster, no (or a minimum number) and then change the cluster centers, and a local minimum squared error.

Caverage
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