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https://issues.apache.org/jira/browse/SPARK-2429?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14172404#comment-14172404
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Yu Ishikawa commented on SPARK-2429:
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I am improving the performance of my code.
Therefore, I inspect the performance converting Spark vectors into breeze
vectors with the simple benchmark.
Please check the benchmark result.
http://apache-spark-developers-list.1001551.n3.nabble.com/mllib-Share-the-simple-benchmark-result-about-the-cast-cost-from-Spark-vector-to-Breeze-vector-td8793.html
> 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: The Result of Benchmarking a Hierarchical Clustering.pdf
>
>
> 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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