``k``
-----

- Available in: K-Means, PCA, GLRM
- Hyperparameter: yes

Description
~~~~~~~~~~~

K-Means
'''''''

In K-Means, a "cluster" refers to groups of data in a dataset that are similar to one another. The K-Means algorithm finds clusters and cluster centers in a set of unlabled data.  

This option specifies the maximum number of clusters that K-Means will form.  If ``estimate_k`` is disabled, the model will find :math:`k` centroids, otherwise it will find up to :math:`k` centroids. This parameter is required and has no default value. 

PCA
'''

In PCA, ``k`` specifies the rank of matrix approximation. This can be a value from 1 to 9 and defaults to 1.

Related Parameters
~~~~~~~~~~~~~~~~~~

K-Means
'''''''

- `estimate_k <estimate_k.html>`__

Example
~~~~~~~

.. tabs::
   .. code-tab:: r R

		library(h2o)
		h2o.init()

		# import the seeds dataset:
		# this dataset looks at three different types of wheat varieties
		# the original dataset can be found at http://archive.ics.uci.edu/ml/datasets/seeds
		seeds <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt")

		# set the predictor names 
		# ignore the 8th column which has the prior known clusters for this dataset
		predictors <- colnames(seeds)[-length(seeds)]

		# split into train and validation
		seeds_splits <- h2o.splitFrame(data = seeds, ratios = 0.8, seed = 1234)
		train <- seeds_splits[[1]]
		valid <- seeds_splits[[2]]

		# try using the `k` parameter:
		# build the model with three clusters
		seeds_kmeans <- h2o.kmeans(x = predictors, k = 3, training_frame = train, validation_frame = valid, seed = 1234)

		# print the total within cluster sum-of-square error for the validation dataset
		print(paste0("Total sum-of-square error for valid dataset: ", h2o.tot_withinss(object = seeds_kmeans, valid = T)))


		# select the values for `k` to grid over:
		# Note: this dataset is too small to see significant differences between these options
		# the purpose of the example is to show how to use grid search with `k` if desired
		hyper_params <- list( k = c(2, 3, 7) )

		# this example uses cartesian grid search because the search space is small
		# and we want to see the performance of all models. For a larger search space use
		# random grid search instead: list(strategy = "RandomDiscrete")
		grid <- h2o.grid(x = predictors, training_frame = train, validation_frame = valid,
		                 algorithm = "kmeans", grid_id = "seeds_grid", hyper_params = hyper_params,
		                 search_criteria = list(strategy = "Cartesian"), seed = 1234)

		## Sort the grid models by TotSS
		sorted_grid <- h2o.getGrid("seeds_grid", sort_by  = "tot_withinss", decreasing = F)
		sorted_grid
	
   .. code-tab:: python

		import h2o
		from h2o.estimators.kmeans import H2OKMeansEstimator
		h2o.init()

		# import the seeds dataset:
		# this dataset looks at three different types of wheat varieties
		# the original dataset can be found at http://archive.ics.uci.edu/ml/datasets/seeds
		seeds = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt")

		# set the predictor names 
		# ignore the 8th column which has the prior known clusters for this dataset
		predictors = seeds.columns[0:7]

		# split into train and validation sets
		train, valid = seeds.split_frame(ratios = [.8], seed = 1234)

		# try using the `k` parameter:
		# build the model with three clusters
		# initialize the estimator then train the model
		seeds_kmeans = H2OKMeansEstimator(k = 3, seed = 1234)
		seeds_kmeans.train(x = predictors, training_frame = train, validation_frame=valid)

		# print the total within cluster sum-of-square error for the validation dataset
		print("Total sum-of-square error for valid dataset:",seeds_kmeans.tot_withinss(valid = True))

		# grid over `k`
		# import Grid Search
		from h2o.grid.grid_search import H2OGridSearch

		# select the values for `k` to grid over
		# Note: this dataset is too small to see significant differences between these options
		# the purpose of the example is to show how to use grid search with `k` if desired
		hyper_params = {'k': [2,3,7]}

		# this example uses cartesian grid search because the search space is small
		# and we want to see the performance of all models. For a larger search space use
		# random grid search instead: {'strategy': "RandomDiscrete"}
		# initialize the estimator
		seeds_kmeans = H2OKMeansEstimator(seed = 1234)

		# build grid search with previously made Kmeans and hyperparameters
		grid = H2OGridSearch(model = seeds_kmeans, hyper_params = hyper_params,
		                     search_criteria = {'strategy': "Cartesian"})

		# train using the grid
		grid.train(x = predictors, training_frame = train, validation_frame = valid)

		# sort the grid models by total within cluster sum-of-square error.
		sorted_grid = grid.get_grid(sort_by='tot_withinss', decreasing=False)
		print(sorted_grid)
