engleski

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 $\mathcal{; ; ; ; ; A}; ; ; ; ; \subset\R^n$ 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; data mining

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