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: This option allows you to specify a dataframe, where each row represents an initial cluster center. The user-
  specified points must have the same number of columns as the training observations. The number of rows must
  equal the number of clusters
  
  Type: ``Union[None, str, H2OFrame]``.
  
  :examples:
  
  >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
  >>> iris['class'] = iris['class'].asfactor()
  >>> predictors = iris.columns[:-1]
  >>> train, valid = iris.split_frame(ratios=[.8], seed=1234)
  >>> point1 = [4.9,3.0,1.4,0.2]
  >>> point2 = [5.6,2.5,3.9,1.1]
  >>> point3 = [6.5,3.0,5.2,2.0]
  >>> points = h2o.H2OFrame([point1, point2, point3])
  >>> iris_km = H2OKMeansEstimator(k=3,
  ...                              user_points=points,
  ...                              seed=1234)
  >>> iris_km.train(x=predictors,
  ...               training_frame=iris,
  ...               validation_frame=valid)
  >>> iris_kmeans.tot_withinss(valid=True)
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code: def user_points(self):
      """
      This option allows you to specify a dataframe, where each row represents an initial cluster center. The user-
      specified points must have the same number of columns as the training observations. The number of rows must
      equal the number of clusters
  
      Type: ``Union[None, str, H2OFrame]``.
  
      :examples:
  
      >>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
      >>> iris['class'] = iris['class'].asfactor()
      >>> predictors = iris.columns[:-1]
      >>> train, valid = iris.split_frame(ratios=[.8], seed=1234)
      >>> point1 = [4.9,3.0,1.4,0.2]
      >>> point2 = [5.6,2.5,3.9,1.1]
      >>> point3 = [6.5,3.0,5.2,2.0]
      >>> points = h2o.H2OFrame([point1, point2, point3])
      >>> iris_km = H2OKMeansEstimator(k=3,
      ...                              user_points=points,
      ...                              seed=1234)
      >>> iris_km.train(x=predictors,
      ...               training_frame=iris,
      ...               validation_frame=valid)
      >>> iris_kmeans.tot_withinss(valid=True)
      """
      return self._parms.get("user_points")
  
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 28 for calling the H2OKMeansEstimator obj's underlying member method code with example usage: obj.user_points() and returns: self