Analysis of the k-means algorithm in the case of data points occurring on the border of two or more clusters
In this paper, the well-known k -means algorithm for searching for a locally optimal partition of the set A?Rn A ? R n is analyzed in the case if some data points occur on the border of two or more clusters. For this special case, a useful strategy by implementation of the k-means algorithm is proposed.
k-means clustering locally optimal partition data mining least square distance-like function cluster border