[SPARK-6137] G-Means clustering algorithm implementation

• Type: New Feature
• Status: Resolved
• Priority: Minor
• Resolution: Won't Fix
• Component/s: MLlib
• Labels: clustering
• Assignee: Unassigned
• Reporter: Denis Dus
• Created: 03/Mar/15 09:54
• Updated: 21/Jan/16 16:26
• Resolved: 21/Jan/16 16:26

Description

Will it be useful to implement G-Means clustering algorithm based on K-Means?
G-means is a powerful extension of k-means, which uses test of cluster data normality to decide if it necessary to split current cluster into new two. It's relative complexity (compared to k-Means) is O(K), where K is maximum number of clusters.

The original paper is by Greg Hamerly and Charles Elkan from University of California:
http://papers.nips.cc/paper/2526-learning-the-k-in-k-means.pdf

I also have a small prototype of this algorithm written in R (if anyone is interested in it).