h2o (version 3.0.0.22)

h2o.kmeans: KMeans Model in H2O

Description

Performs k-means clustering on an H2O dataset.

Usage

h2o.kmeans(training_frame, x, k, model_id, max_iterations = 1000,
  standardize = TRUE, init = c("Furthest", "Random", "PlusPlus"), seed)

Arguments

training_frame

An H2OFrame object containing the variables in the model.

x

(Optional) A vector containing the data columns on which k-means operates.

k

The number of clusters. Must be between 1 and 1e7 inclusive. k may be omitted if the user specifies the initial centers in the init parameter. If k is not omitted, in this case, then it should be equal to the number of user-specified centers.

model_id

(Optional) The unique id assigned to the resulting model. If none is given, an id will automatically be generated.

max_iterations

The maximum number of iterations allowed. Must be between 0

standardize

Logical, indicates whether the data should be standardized before running k-means.

init

A character string that selects the initial set of k cluster centers. Possible values are "Random": for random initialization, "PlusPlus": for k-means plus initialization, or "Furthest": for initialization at the furthest point from each successive center

seed

(Optional) Random seed used to initialize the cluster centroids.

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)
localH2O <- h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(localH2O, path = prosPath)
h2o.kmeans(training_frame = prostate.hex, k = 10, x = c("AGE", "RACE", "VOL", "GLEASON"))