[ https://issues.apache.org/jira/browse/SPARK-5405?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng updated SPARK-5405: --------------------------------- Labels: clustering (was: ) > Spark clusterer should support high dimensional data > ---------------------------------------------------- > > Key: SPARK-5405 > URL: https://issues.apache.org/jira/browse/SPARK-5405 > Project: Spark > Issue Type: New Feature > Components: MLlib > Affects Versions: 1.2.0 > Reporter: Derrick Burns > Labels: clustering > Original Estimate: 504h > Remaining Estimate: 504h > > The MLLIB clusterer works well for low (<200) dimensional data. However, > performance is linear with the number of dimensions. So, for practical > purposes, it is not very useful for high dimensional data. > Depending on the data type, one can embed the high dimensional data into > lower dimensional spaces in a distance-preserving way. The Spark clusterer > should support such embedding. > An example implementation that supports high dimensional data is here: > https://github.com/derrickburns/generalized-kmeans-clustering -- 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