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"))
character
names of the predictors in the model.Logical
. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.Logical
. Whether to keep the cross-validation fold assignment. Defaults to FALSE.Logical
. Ignore constant columns. Defaults to TRUE.Logical
. Whether to score during each iteration of model training. Defaults to FALSE.Logical
. Whether to estimate the number of clusters (Logical
. Standardize columns before computing distances Defaults to TRUE.h2o.cluster_sizes
, h2o.totss
, h2o.num_iterations
,
h2o.betweenss
, h2o.tot_withinss
, h2o.withinss
,
h2o.centersSTD
, h2o.centers
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"))
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