[SPARK-4039] KMeans support sparse cluster centers

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Won't Fix
• Affects Version/s: 1.1.0
• Component/s: MLlib
• Labels: clustering
• Assignee: Unassigned
• Reporter: Antoine Amend
• Created: 21/Oct/14 19:51
• Updated: 27/Feb/16 19:56
• Resolved: 03/Dec/15 15:28

Description

When the number of features is not known, it might be quite helpful to create sparse vectors using HashingTF.transform. KMeans transforms centers vectors to dense vectors (https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L307), therefore leading to OutOfMemory (even with small k).

Any way to keep vectors sparse ?

Issue Links

• is duplicated by: SPARK-12861 Changes to support KMeans with large feature space