请问spss或者sas初始聚类中心的算法是怎么算的？双色球的秘密

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请问spss或者sas初始聚类中心的算法是什么？我那双色球的红球历史数据做了k-means的聚类。初始聚类中心为：

最终聚类中心为：

如何才能知道他的初始聚类中心算法？

我找到了初始聚类中心的算法啦，可是我不懂他的意思，有高人可以给解释一下吗？以下是初始聚类中心算法的英文解释。
If minid(xk,Mi)>dmn and d(xk,Mm)>d(xk,Mn), then xk replaces Mn. If minid(xk,Mi)>dmn and d(xk,Mm)<d(xk,Mn), then xk replaces Mm; that is, if the distance between xk and its closest cluster mean is greater than the distance between the two closest means (Mm and Mn), then xk replaces either Mm or Mn, whichever is closer to xk.
If xk does not replace a cluster mean in (a), a second test is made:
Let Mq be the closest cluster mean to xk.
Let Mp be the second closest cluster mean to xk.
If d(xk,Mp)>minid(Mq,Mi), then Mq=xk;
That is, if xk is further from the second closest cluster’s center than the closest cluster’s center is from any other cluster’s center, replace the closest cluster’s center with xk.
At the end of one pass through the data, the initial means of all NC clusters are set. Note that if NOINITIAL is specified, the first NC cases with no missing values are the initial cluster means.

有谁能告诉我是怎么算的吗

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关键词：SPSS 双色球 PSS specified clusters 算法 双色球 中心

我找到了初始聚类中心的算法啦，可是我不懂他的意思，有高人可以给解释一下吗？以下是初始聚类中心算法的英文解释。
If minid(xk,Mi)>dmn and d(xk,Mm)>d(xk,Mn), then xk replaces Mn. If minid(xk,Mi)>dmn and d(xk,Mm)<d(xk,Mn), then xk replaces Mm; that is, if the distance between xk and its closest cluster mean is greater than the distance between the two closest means (Mm and Mn), then xk replaces either Mm or Mn, whichever is closer to xk.

If xk does not replace a cluster mean in (a), a second test is made:
Let Mq be the closest cluster mean to xk.
Let Mp be the second closest cluster mean to xk.
If d(xk,Mp)>minid(Mq,Mi), then Mq=xk;
That is, if xk is further from the second closest cluster’s center than the closest cluster’s center is from any other cluster’s center, replace the closest cluster’s center with xk.

At the end of one pass through the data, the initial means of all NC clusters are set. Note that if NOINITIAL is specified, the first NC cases with no missing values are the initial cluster means.

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