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https://issues.apache.org/jira/browse/SPARK-6509?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14378323#comment-14378323
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Apache Spark commented on SPARK-6509:
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User 'sramirez' has created a pull request for this issue:
https://github.com/apache/spark/pull/5170
> MDLP discretizer
> ----------------
>
> Key: SPARK-6509
> URL: https://issues.apache.org/jira/browse/SPARK-6509
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Sergio RamÃrez-Gallego
>
> Minimum Description Lenght Discretizer
> This method implements Fayyad's discretizer [1] based on Minimum Description
> Length Principle (MDLP) in order to treat non discrete datasets from a
> distributed perspective. We have developed a distributed version from the
> original one performing some important changes.
> -- Improvements on discretizer:
> Support for sparse data.
> Multi-attribute processing. The whole process is carried out in a single
> step when the number of boundary points per attribute fits well in one
> partition (<= 100K boundary points per attribute).
> Support for attributes with a huge number of boundary points (> 100K
> boundary points per attribute). Rare situation.
> This software has been proved with two large real-world datasets such as:
> A dataset selected for the GECCO-2014 in Vancouver, July 13th, 2014
> competition, which comes from the Protein Structure Prediction field
> (http://cruncher.ncl.ac.uk/bdcomp/). The dataset has 32 million instances,
> 631 attributes, 2 classes, 98% of negative examples and occupies, when
> uncompressed, about 56GB of disk space.
> Epsilon dataset:
> http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#epsilon.
> 400K instances and 2K attributes
> We have demonstrated that our method performs 300 times faster than the
> sequential version for the first dataset, and also improves the accuracy for
> Naive Bayes.
> References
> [1] Fayyad, U., & Irani, K. (1993).
> "Multi-interval discretization of continuous-valued attributes for
> classification learning."
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