[jira] [Commented] (SPARK-2429) Hierarchical Implementation of KMeans

["RJ Nowling (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-2429?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14112222#comment-14112222 ] 

RJ Nowling commented on SPARK-2429:
-----------------------------------

Discussion on the dev list mentioned a community preference for implementing KMeans recursively (a divisive approach).  Jeremy Freeman provided an example here:

https://gist.github.com/freeman-lab/5947e7c53b368fe90371

The example needs to be optimized but provided a good starting point.  For example, every time KMeans is called, the data is converted to Breeze Vectors.

Here are two papers on divisive Kmeans:

A combined K-means and hierarchical clustering method for improving the clustering efficiency of microarray (2005) by Chen, et al.

Divisive Hierarchical K-Means (2006) by Lamrous, et al.


> Hierarchical Implementation of KMeans
> -------------------------------------
>
>                 Key: SPARK-2429
>                 URL: https://issues.apache.org/jira/browse/SPARK-2429
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: RJ Nowling
>            Priority: Minor
>
> Hierarchical clustering algorithms are widely used and would make a nice addition to MLlib.  Clustering algorithms are useful for determining relationships between clusters as well as offering faster assignment. Discussion on the dev list suggested the following possible approaches:
> * Top down, recursive application of KMeans
> * Reuse DecisionTree implementation with different objective function
> * Hierarchical SVD
> It was also suggested that support for distance metrics other than Euclidean such as negative dot or cosine are necessary.



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