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Posted to issues@spark.apache.org by "RJ Nowling (JIRA)" <ji...@apache.org> on 2015/03/02 22:36:06 UTC
[jira] [Commented] (SPARK-2429) Hierarchical Implementation of
KMeans
[ https://issues.apache.org/jira/browse/SPARK-2429?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14343806#comment-14343806 ]
RJ Nowling commented on SPARK-2429:
-----------------------------------
[~yuu.ishikawa@gmail.com] are you still working on this? 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
> Labels: clustering
> 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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