[jira] [Created] (SPARK-3012) Standardized Distance Functions between two Vectors for MLlib

["Yu Ishikawa (JIRA)" <ji...@apache.org>]

Yu Ishikawa created SPARK-3012:
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             Summary: 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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