GCMeans: Generalized C-means

Description Usage Arguments Value Examples

View source: R/GFCM.R

Description

The generalized c-mean algorithm

Usage

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GCMeans(
  data,
  k,
  m,
  beta,
  maxiter = 500,
  tol = 0.01,
  standardize = TRUE,
  verbose = TRUE,
  init = "random",
  seed = NULL
)

Arguments

data

A dataframe with only numerical variable

k

An integer describing the number of cluster to find

m

A float for the fuzziness degree

beta

A float for the beta parameter (control speed convergence and classification crispness)

maxiter

A float for the maximum number of iteration

tol

The tolerance criterion used in the evaluateMatrices function for convergence assessment

standardize

A boolean to specify if the variables must be centered and reduced (default = True)

verbose

A boolean to specify if the messages should be displayed

init

A string indicating how the initial centers must be selected. "random" indicates that random observations are used as centers. "kpp" use a distance based method resulting in more dispersed centers at the beginning. Both of them are heuristic.

seed

An integer used for random number generation. It ensures that the start centers will be the same if the same integer is selected.

Value

A named list with :

Examples

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data(LyonIris)
AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img",
"TxChom1564","Pct_brevet","NivVieMed")
dataset <- LyonIris@data[AnalysisFields]
result <- GCMeans(dataset,k = 5, m = 1.5, beta = 0.5, standardize = TRUE)

geocmeans documentation built on April 21, 2021, 9:07 a.m.