Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/10150#discussion_r49251302
--- Diff: python/pyspark/mllib/clustering.py ---
@@ -38,13 +38,120 @@
from pyspark.mllib.util import Saveable, Loader, inherit_doc, JavaLoader,
JavaSaveable
from pyspark.streaming import DStream
-__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel',
'GaussianMixture',
- 'PowerIterationClusteringModel', 'PowerIterationClustering',
- 'StreamingKMeans', 'StreamingKMeansModel',
+__all__ = ['BisectingKMeansModel', 'BisectingKMeans', 'KMeansModel',
'KMeans',
+ 'GaussianMixtureModel', 'GaussianMixture',
'PowerIterationClusteringModel',
+ 'PowerIterationClustering', 'StreamingKMeans',
'StreamingKMeansModel',
'LDA', 'LDAModel']
@inherit_doc
+class BisectingKMeansModel(JavaModelWrapper):
+ """
+ .. note:: Experimental
+
+ A clustering model derived from the bisecting k-means method.
+
+ >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2)
+ >>> bskm = BisectingKMeans()
+ >>> model = bskm.train(sc.parallelize(data), k=4)
--- End diff --
Specify number of partitions for sc.parallelize; not doing so has caused
flaky tests in the past (because of randomization interacting with
partitioning).
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