[SPARK-3219] K-Means clusterer should support Bregman distance functions

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Won't Fix
• Component/s: MLlib
• Labels: clustering
• Assignee: Derrick Burns
• Reporter: Derrick Burns
• Created: 26/Aug/14 01:29
• Updated: 10/Jul/15 07:41
• Resolved: 10/Jul/15 07:41

Description

The K-Means clusterer supports the Euclidean distance metric. However, it is rather straightforward to support Bregman (http://machinelearning.wustl.edu/mlpapers/paper_files/BanerjeeMDG05.pdf) distance functions which would increase the utility of the clusterer tremendously.

I have modified the clusterer to support pluggable distance functions. However, I notice that there are hundreds of outstanding pull requests. If someone is willing to work with me to sponsor the work through the process, I will create a pull request. Otherwise, I will just keep my own fork.

Issue Links

• relates to: SPARK-3012 Standardized Distance Functions between two Vectors for MLlib
• links to: [Github] Pull Request #2419 (derrickburns)
• links to: [Github] Pull Request #2634 (derrickburns)