Performs k-means clustering on an H2O dataset

Source: R/kmeans.R

Performs k-means clustering on an H2O dataset

h2o.kmeans(training_frame, x, model_id = NULL, validation_frame = NULL,
  nfolds = 0, keep_cross_validation_predictions = FALSE,
  keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO",
  "Random", "Modulo", "Stratified"), fold_column = NULL,
  ignore_const_cols = TRUE, score_each_iteration = FALSE, k = 1,
  estimate_k = FALSE, user_points = NULL, max_iterations = 10,
  standardize = TRUE, seed = -1, init = c("Random", "PlusPlus",
  "Furthest", "User"), max_runtime_secs = 0,
  categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
  "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"))

Arguments

training_frame	Id of the training data frame.
x	A vector containing the character names of the predictors in the model.
model_id	Destination id for this model; auto-generated if not specified.
validation_frame	Id of the validation data frame.
nfolds	Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0.
keep_cross_validation_predictions	Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment	Logical. Whether to keep the cross-validation fold assignment. Defaults to FALSE.
fold_assignment	Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.
fold_column	Column with cross-validation fold index assignment per observation.
ignore_const_cols	Logical. Ignore constant columns. Defaults to TRUE.
score_each_iteration	Logical. Whether to score during each iteration of model training. Defaults to FALSE.
k	The max. number of clusters. If estimate_k is disabled, the model will find k centroids, otherwise it will find up to k centroids. Defaults to 1.
estimate_k	Logical. Whether to estimate the number of clusters (<=k) iteratively and deterministically. Defaults to FALSE.
user_points	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
max_iterations	Maximum training iterations (if estimate_k is enabled, then this is for each inner Lloyds iteration) Defaults to 10.
standardize	Logical. Standardize columns before computing distances Defaults to TRUE.
seed	Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default) Defaults to -1 (time-based random number).
init	Initialization mode Must be one of: "Random", "PlusPlus", "Furthest", "User". Defaults to Furthest.
max_runtime_secs	Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
categorical_encoding	Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.

Value

Returns an object of class H2OClusteringModel.

See also

h2o.cluster_sizes, h2o.totss, h2o.num_iterations, h2o.betweenss, h2o.tot_withinss, h2o.withinss, h2o.centersSTD, h2o.centers

Examples

# NOT RUN {
library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prosPath)
h2o.kmeans(training_frame = prostate.hex, k = 10, x = c("AGE", "RACE", "VOL", "GLEASON"))
# }