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From "Yu Ishikawa (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-2429) Hierarchical Implementation of KMeans
Date Thu, 09 Oct 2014 12:18:34 GMT

     [ https://issues.apache.org/jira/browse/SPARK-2429?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Yu Ishikawa updated SPARK-2429:
-------------------------------
    Attachment: The Result of Benchmarking a Hierarchical Clustering.pdf

Sorry for making some mistakes. I fixed them.

- Cluster Spec
- Typy mistakes

> 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, 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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