class_docstr: K-means
  
  Performs k-means clustering on an H2O dataset.
class_docstr_tok: ["K-means","Performs","k-means","clustering","on","an","H2O","dataset","."]
docstr: An array specifying the minimum number of points that should be in each cluster. The length of the constraints
  array has to be the same as the number of clusters.
  
  Type: ``List[int]``.
  
  :examples:
  
  >>> iris_h2o = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
  >>> k=3
  >>> start_points = h2o.H2OFrame(
  ...         [[4.9, 3.0, 1.4, 0.2],
  ...          [5.6, 2.5, 3.9, 1.1],
  ...          [6.5, 3.0, 5.2, 2.0]])
  >>> kmm = H2OKMeansEstimator(k=k,
  ...                          user_points=start_points,
  ...                          standardize=True,
  ...                          cluster_size_constraints=[2, 5, 8],
  ...                          score_each_iteration=True)
  >>> kmm.train(x=list(range(7)), training_frame=iris_h2o)
  >>> kmm.scoring_history()
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code: def cluster_size_constraints(self):
      """
      An array specifying the minimum number of points that should be in each cluster. The length of the constraints
      array has to be the same as the number of clusters.
  
      Type: ``List[int]``.
  
      :examples:
  
      >>> iris_h2o = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
      >>> k=3
      >>> start_points = h2o.H2OFrame(
      ...         [[4.9, 3.0, 1.4, 0.2],
      ...          [5.6, 2.5, 3.9, 1.1],
      ...          [6.5, 3.0, 5.2, 2.0]])
      >>> kmm = H2OKMeansEstimator(k=k,
      ...                          user_points=start_points,
      ...                          standardize=True,
      ...                          cluster_size_constraints=[2, 5, 8],
      ...                          score_each_iteration=True)
      >>> kmm.train(x=list(range(7)), training_frame=iris_h2o)
      >>> kmm.scoring_history()
      """
      return self._parms.get("cluster_size_constraints")
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 44 for calling the H2OKMeansEstimator obj's underlying member method code with example usage: obj.cluster_size_constraints() and returns: self