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From "RJ Nowling (JIRA)" <>
Subject [jira] [Commented] (SPARK-2429) Hierarchical Implementation of KMeans
Date Wed, 29 Oct 2014 13:20:34 GMT


RJ Nowling commented on SPARK-2429:

The sparsity tests look good.  Have you compared training and assignment time to KMeans yet?
 An improvement in the assignment time will be important.  Also, I don't see a breakdown of
the total time by splitting clusters, assignments, etc.  Doesn't need to be for every combination
of parameters just one or two.  That would be very helpful.  Thanks!

> Hierarchical Implementation of KMeans
> -------------------------------------
>                 Key: SPARK-2429
>                 URL:
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: RJ Nowling
>            Assignee: Yu Ishikawa
>            Priority: Minor
>         Attachments: 2014-10-20_divisive-hierarchical-clustering.pdf, The Result of Benchmarking
a Hierarchical Clustering.pdf, benchmark-result.2014-10-29.html, benchmark2.html
> 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
> * 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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