Denis Dus created SPARK-6137:
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Summary: G-Means clustering algorithm implementation
Key: SPARK-6137
URL: https://issues.apache.org/jira/browse/SPARK-6137
Project: Spark
Issue Type: New Feature
Components: MLlib
Reporter: Denis Dus
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).
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