I would say for bigdata applications the most useful would be hierarchical k-means with back tracking and the ability to support k nearest centroids.
On Tue, Jul 8, 2014 at 10:54 AM, RJ Nowling <rnowl...@gmail.com> wrote: > Hi all, > > MLlib currently has one clustering algorithm implementation, KMeans. > It would benefit from having implementations of other clustering > algorithms such as MiniBatch KMeans, Fuzzy C-Means, Hierarchical > Clustering, and Affinity Propagation. > > I recently submitted a PR [1] for a MiniBatch KMeans implementation, > and I saw an email on this list about interest in implementing Fuzzy > C-Means. > > Based on Sean Owen's review of my MiniBatch KMeans code, it became > apparent that before I implement more clustering algorithms, it would > be useful to hammer out a framework to reduce code duplication and > implement a consistent API. > > I'd like to gauge the interest and goals of the MLlib community: > > 1. Are you interested in having more clustering algorithms available? > > 2. Is the community interested in specifying a common framework? > > Thanks! > RJ > > [1] - https://github.com/apache/spark/pull/1248 > > > -- > em rnowl...@gmail.com > c 954.496.2314 > -- Yee Yang Li Hector <http://google.com/+HectorYee> *google.com/+HectorYee <http://google.com/+HectorYee>*
