c_CMeans: Fuzzy C-means Clustering

In egenn/rtemis: Machine Learning and Visualization

Fuzzy C-means Clustering

Description

Perform fuzzy C-means clustering using e1071::cmeans

Usage

c_CMeans(
  x,
  k = 2,
  iter.max = 100,
  dist = "euclidean",
  method = "cmeans",
  m = 2,
  rate.par = NULL,
  weights = 1,
  control = list(),
  verbose = TRUE,
  ...
)

Arguments

x	Input data
k	Integer: Number of clusters to get. Default = 2
iter.max	Integer: Maximum number of iterations. Default = 100
dist	Character: Distance measure to use: 'euclidean' or 'manhattan'. Default = "euclidean"
method	Character: "cmeans" - fuzzy c-means clustering; "ufcl": on-line update. Default = "cmeans"
m	Float (>1): Degree of fuzzification. Default = 2
rate.par	Float (0, 1): Learning rate for the online variant. (Default = .3)
weights	Float (>0): Case weights
control	List of control parameters. See e1071::cmeans
verbose	Logical: If TRUE, print messages to console
...	Additional parameters to be passed to e1071::cmeans

Value

rtClust object

Author(s)

E.D. Gennatas

See Also

Other Clustering: c_DBSCAN(), c_EMC(), c_H2OKMeans(), c_HARDCL(), c_HOPACH(), c_KMeans(), c_MeanShift(), c_NGAS(), c_PAM(), c_PAMK(), c_SPEC()

egenn/rtemis documentation built on Oct. 28, 2024, 6:30 a.m.