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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