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https://issues.apache.org/jira/browse/SPARK-2429?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14207463#comment-14207463
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Yu Ishikawa commented on SPARK-2429:
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[~rnowling] Sorry for commenting again. Could you tell me what you think about
the new function to cut a dendrogram?
(For example. you think we don't need the new function. Or we should make an
advantage against KMeans from another point of view. )
1. This algorithm doesn't have an advantage about elapsed time of assignment
against KMeans.
2. The new function generate another model to cut a dendrogram by height
without re-training by another parameters.
Thanks,
> 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
> 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 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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