c_H2OKMeans: K-Means Clustering with H2O

In egenn/rtemis: Machine Learning and Visualization

K-Means Clustering with H2O

Description

Perfomr K-Means clustering using h2o::h2o.kmeans

Usage

c_H2OKMeans(
  x,
  x.test = NULL,
  k = 2,
  estimate.k = FALSE,
  nfolds = 0,
  max.iterations = 10,
  ip = "localhost",
  port = 54321,
  n.cores = rtCores,
  seed = -1,
  init = c("Furthest", "Random", "PlusPlus", "User"),
  categorical.encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary",
    "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
  verbose = TRUE,
  ...
)

Arguments

x	Input matrix / data.frame
x.test	Testing set matrix / data.frame
k	Integer: Number of clusters to get
estimate.k	Logical: if TRUE, estimate k up to a maximum set by the k argument
nfolds	Integer: Number of cross-validation folds
max.iterations	Integer: Maximum number of iterations
ip	Character: IP address of H2O server. Default = "localhost"
port	Integer: Port number of H2O server. Default = 54321
n.cores	Integer: Number of cores to use
seed	Integer: Seed for H2O's random number generator. Default = -1 (time-based ranodm number)
init	Character: Initialization mode: "Furthest", "Random", "PlusPlus", "User". Default = "Furthest"
categorical.encoding	Character: How to encode categorical variables: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Default = "AUTO"
verbose	Logical: If TRUE, print messages to screen
...	Additional arguments to pass to h2p::h2o.kmeans

Details

Check out the H2O Flow at ⁠[ip]:[port]⁠, Default IP:port is "localhost:54321" e.g. if running on localhost, point your web browser to localhost:54321 For additional information, see help on h2o::h2o.kmeans

Value

rtMod object

Author(s)

E.D. Gennatas

See Also

Other Clustering: c_CMeans(), c_DBSCAN(), c_EMC(), c_HARDCL(), c_HOPACH(), c_KMeans(), c_MeanShift(), c_NGAS(), c_PAM(), c_PAMK(), c_SPEC()

egenn/rtemis documentation built on Nov. 22, 2024, 4:12 a.m.