[jira] [Commented] (FLINK-2131) Add Initialization schemes for K-means clustering

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-2131?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14654530#comment-14654530 ] 

ASF GitHub Bot commented on FLINK-2131:
---------------------------------------

Github user sachingoel0101 commented on the pull request:

    https://github.com/apache/flink/pull/757#issuecomment-127796214
  
    This now implements the complete KMeans clustering algorithm along-with four initialization strategies, namely, from given centroids, random, kmeans++ and kmeans||.
    I'd like to see this merged soon. It's an important algorithm for the ML library.


> Add Initialization schemes for K-means clustering
> -------------------------------------------------
>
>                 Key: FLINK-2131
>                 URL: https://issues.apache.org/jira/browse/FLINK-2131
>             Project: Flink
>          Issue Type: Task
>          Components: Machine Learning Library
>            Reporter: Sachin Goel
>            Assignee: Sachin Goel
>
> The Lloyd's [KMeans] algorithm takes initial centroids as its input. However, in case the user doesn't provide the initial centers, they may ask for a particular initialization scheme to be followed. The most commonly used are these:
> 1. Random initialization: Self-explanatory
> 2. kmeans++ initialization: http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf
> 3. kmeans|| : http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
> For very large data sets, or for large values of k, the kmeans|| method is preferred as it provides the same approximation guarantees as kmeans++ and requires lesser number of passes over the input data.



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