Hi all, At present, all the clustering algorithms in MLlib require the number of clusters to be specified in advance.
The Dirichlet process (DP) is a popular non-parametric Bayesian mixture model that allows for flexible clustering of data without having to specify apriori the number of clusters. DP means is a non-parametric clustering algorithm that uses a scale parameter 'lambda' to control the creation of new clusters. We have followed the distributed implementation of DP means which has been proposed in the paper titled "MLbase: Distributed Machine Learning Made Easy" by Xinghao Pan, Evan R. Sparks, Andre Wibisono. I have raised a JIRA ticket at https://issues.apache.org/jira/browse/SPARK-8402 Suggestions and guidance are welcome. Regards, Meethu Mathew Senior Engineer Flytxt www.flytxt.com | Visit our blog <http://blog.flytxt.com/> | Follow us <http://www.twitter.com/flytxt> | Connect on LinkedIn <http://www.linkedin.com/company/22166?goback=%2Efcs_GLHD_flytxt_false_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2&trk=ncsrch_hits>
