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Posted to issues@spark.apache.org by "Sorin Ciolofan (JIRA)" <ji...@apache.org> on 2015/08/14 22:22:46 UTC
[jira] [Commented] (SPARK-4038) Outlier Detection Algorithm for
MLlib
[ https://issues.apache.org/jira/browse/SPARK-4038?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14697690#comment-14697690 ]
Sorin Ciolofan commented on SPARK-4038:
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Hello!
Just want to add that k-means clustering is highly influenced by the presence of outliers so it may provide false results (false positives or false negatives for outlier detection). For this reason it was proposed a modified version k-mean-- which computes in parallel clusters and outliers (http://pmg.it.usyd.edu.au/outliers.pdf).
But one of the best and very promising is a rapid detection of outliers via random sampling (http://papers.nips.cc/paper/5127-rapid-distance-based-outlier-detection-via-sampling.pdf) for which I was proposing a parallel implementation on Map Reduce in one of the papers that should be published soon. It is a distance based method, providing also a rank for outlierness. I am wondering if would not be possible to have an implementation in Spark of this method.
Regards
Sorin
> 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.
>
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