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Anant Daksh Asthana commented on SPARK-4038: -------------------------------------------- I do agree a general wrapper might be quite involved. It may be wise to create a toolkit of algorithms and just document them well. Follow the patterns to make them all compatible with mlpipe. What do you think of that? On Mon, Jun 22, 2015, 4:14 PM Joseph K. Bradley (JIRA) <j...@apache.org> > Outlier Detection Algorithm for MLlib > ------------------------------------- > > Key: SPARK-4038 > URL: https://issues.apache.org/jira/browse/SPARK-4038 > Project: Spark > Issue Type: New Feature > Components: MLlib > Reporter: Ashutosh Trivedi > Priority: Minor > > The aim of this JIRA is to discuss about which parallel outlier detection > algorithms can be included in MLlib. > The one which I am familiar with is Attribute Value Frequency (AVF). It > scales linearly with the number of data points and attributes, and relies on > a single data scan. It is not distance based and well suited for categorical > data. In original paper a parallel version is also given, which is not > complected to implement. I am working on the implementation and soon submit > the initial code for review. > Here is the Link for the paper > http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4410382 > As pointed out by Xiangrui in discussion > http://apache-spark-developers-list.1001551.n3.nabble.com/MLlib-Contributing-Algorithm-for-Outlier-Detection-td8880.html > There are other algorithms also. Lets discuss about which will be more > general and easily paralleled. > -- 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