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Posted to issues@spark.apache.org by "Yu Ishikawa (JIRA)" <ji...@apache.org> on 2014/11/06 06:43:34 UTC
[jira] [Closed] (SPARK-3012) Standardized Distance Functions
between two Vectors for MLlib
[ https://issues.apache.org/jira/browse/SPARK-3012?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Yu Ishikawa closed SPARK-3012.
------------------------------
Resolution: Fixed
> Standardized Distance Functions between two Vectors for MLlib
> -------------------------------------------------------------
>
> Key: SPARK-3012
> URL: https://issues.apache.org/jira/browse/SPARK-3012
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Yu Ishikawa
> Priority: Minor
>
> Most of the clustering algorithms need distance functions between two Vectors.
> We should include the standardized distance function library in MLlib.
> I think that the standardized distance functions help us to implement more machine learning algorithms efficiently.
> h3. For example
> - Chebyshev Distance
> - Cosine Distance
> - Euclidean Distance
> - Mahalanobis Distance
> - Manhattan Distance
> - Minkowski Distance
> - SquaredEuclidean Distance
> - Tanimoto Distance
> - Weighted Distance
> - WeightedEuclidean Distance
> - WeightedManhattan Distance
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