[GitHub] spark pull request: [SPARK-9883][MLlib] Distance to each kmean clu...

[BertrandDechoux <gi...@git.apache.org>]

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.
    



---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---

---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org