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: Initialization mode
  
  Type: ``Literal["random", "plus_plus", "furthest", "user"]``, defaults to ``"furthest"``.
  
  :examples:
  
  >>> seeds = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt")
  >>> predictors = seeds.columns[0:7]
  >>> train, valid = seeds.split_frame(ratios=[.8], seed=1234)
  >>> seeds_km = H2OKMeansEstimator(k=3,
  ...                               init='Furthest',
  ...                               seed=1234)
  >>> seeds_km.train(x=predictors,
  ...                training_frame=train,
  ...                validation_frame= valid)
  >>> seeds_km.scoring_history()
docstr_tok: ["Initialization","mode","Type",":","``","Literal","[","``","random","''",",","``","plus_plus","''",",","``","furthest","''",",","``","user","''","]","``",",","defaults","to","``","``","furthest","''","``",".",":","examples",":",">",">",">","seeds","=","h2o.import_file","(","``","https",":","\/\/s3.amazonaws.com\/h2o-public-test-data\/smalldata\/flow_examples\/seeds_dataset.txt","''",")",">",">",">","predictors","=","seeds.columns","[","0:7","]",">",">",">","train",",","valid","=","seeds.split_frame","(","ratios=","[",".8","]",",","seed=1234",")",">",">",">","seeds_km","=","H2OKMeansEstimator","(","k=3",",","...","init='Furthest","'",",","...","seed=1234",")",">",">",">","seeds_km.train","(","x=predictors",",","...","training_frame=train",",","...","validation_frame=","valid",")",">",">",">","seeds_km.scoring_history","(",")"]
code: def init(self):
      """
      Initialization mode
  
      Type: ``Literal["random", "plus_plus", "furthest", "user"]``, defaults to ``"furthest"``.
  
      :examples:
  
      >>> seeds = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt")
      >>> predictors = seeds.columns[0:7]
      >>> train, valid = seeds.split_frame(ratios=[.8], seed=1234)
      >>> seeds_km = H2OKMeansEstimator(k=3,
      ...                               init='Furthest',
      ...                               seed=1234)
      >>> seeds_km.train(x=predictors,
      ...                training_frame=train,
      ...                validation_frame= valid)
      >>> seeds_km.scoring_history()
      """
      return self._parms.get("init")
  
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 36 for calling the H2OKMeansEstimator obj's underlying member method code with example usage: obj.init() and returns: self