setup_CMeans: Setup CMeansConfig

In rtemis: Schema-Driven Machine Learning and Visualization

Setup CMeansConfig

Description

Setup CMeansConfig

Usage

setup_CMeans(
  k = 2L,
  max_iter = 100L,
  dist = "euclidean",
  method = "cmeans",
  m = 2,
  rate_par = NULL,
  weights = 1,
  control = list(),
  features = NULL
)

Arguments

k	Integer [1, Inf): Number of clusters.
max_iter	Integer [1, Inf): Maximum number of iterations.
dist	Character {"euclidean", "manhattan"}: Distance measure to use.
method	Character {"cmeans", "ufcl"}: "cmeans" - fuzzy c-means clustering; "ufcl": on-line update.
m	Numeric (1, Inf): Degree of fuzzification.
rate_par	Optional Numeric [0, 1]: Learning rate for the online variant.
weights	Numeric vector: Case weights. Either a scalar, applied to every case, or a vector with one value per case.
control	List: Control config for clustering algorithm.
features	Optional Character vector: Names of at least 2 distinct feature columns to cluster on. NULL clusters on all numeric features.

Value

CMeansConfig object.

Author(s)

EDG

Examples

cmeans_config <- setup_CMeans(k = 4L, dist = "euclidean")
cmeans_config

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.