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Dhaval Modi commented on SPARK-3012: ------------------------------------ I have implemented Mahalanobis Distance in Spark 1.6.2+ using Breeze 0.12 libraries. One can refer below scala code at: https://github.com/dhmodi/MahalanobisDistance > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org