  This article briefly introduced Arthur and Vassilvitshii's work on
\textbf{k-means++} algorithm and further generalized the center initialization
process. It is found that choosing the most distant sample point from the
nearest center as new center can mostly have the same effect as the center
initialization process in the \textbf{k-means++} algorithm.
