Meethu Mathew created SPARK-8402:
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Summary: DP means clustering
Key: SPARK-8402
URL: https://issues.apache.org/jira/browse/SPARK-8402
Project: Spark
Issue Type: New Feature
Components: MLlib
Reporter: Meethu Mathew
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["Revisiting k-means: New
Algorithms via Bayesian Nonparametrics" by Brian Kulis, Michael I. Jordan].
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.
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