h2o (version 3.10.3.3)

h2o.kmeans: Performs k-means clustering on an H2O dataset.

Description

Performs k-means clustering on an H2O dataset.

Usage

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"))

Arguments

training_frame

Id of the training data frame (Not required, to allow initial validation of model parameters).

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 N-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 (

user_points

User-specified points

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". 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

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"))