Github user BertrandDechoux commented on the pull request:
https://github.com/apache/spark/pull/8849#issuecomment-160952703
In a perfect world, each point belongs to a specific cluster and the number
of clusters is easy to find. In reality, it is less so. Knowing the distance is
a way to appreciate the closeness of a point with regard to a cluster.
K-means can be thought as a special mixture model. When using a mixture
model, the impact of each 'cluster' with regard to a specific point is an
important information. I think the same holds true for K-means.
But, in the end, it does depend in which context and how you are using
K-means indeed.
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