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The aim of the k-means is to minimize squared sum of Euclidean distance fromthe mean (SSEDM) of each cluster. The k-means can effectively optimize thisfunction, but it is too sensitive for initial centers (seeds). This paperproposed a method for initialization of the k-means using the concept of usefulnearest center for each data point.

An initialization method for the k-means using the concept of useful nearest centers