[ https://issues.apache.org/jira/browse/SPARK-4038?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-4038. ------------------------------ Resolution: Won't Fix > 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 (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org