ifcm: Intuitionistic Fuzzy C-Means Clustering
In busenurk/rinfis: Intuitionistic Fuzzy Inference Package

Description Usage Arguments Details Value Author(s) References Examples

View source: R/ifcm.R

Intuitionistic fuzzy c-means clustering algorithm is an extension of fuzzy c-means clustering algorithm.

ifcm(
  x,
  c,
  m = 2,
  alpha = 0.85,
  maxitr = 100,
  epsilon = 0.03,
  fgen = c("Yager", "Sugeno"),
  lambda = 2,
  verbose = FALSE,
  standardize = TRUE
)

`x`	The data matrix where columns correspond to variables and rows to observations.
`c`	Number of clusters
`m`	A number greater than 1 giving the degree of fuzzification.
`alpha`	degree of hesitation
`maxitr`	Maximum number of iterations.
`epsilon`	Error value
`fgen`	The type of intuitionistic fuzzy generator. `Yager` or `Sugeno` type fuzzy generators can be selected. (Default="Yager")
`lambda`	The constant
`verbose`	If TRUE, make some output during learning.
`standardize`	If TRUE the data matrix is standardized. If variables are in the same units already, you might not wish to standardize. Default is `standardize=TRUE`.

The data given by x is clustered by generalized versions of the fuzzy c-means algorithm, If verbose is TRUE, each iteration displays its number and the value of the objective function. The parameters m defines the degree of fuzzification. It is defined for real values greater than 1 and the bigger it is the more fuzzy the membership values of the clustered data points are.

An object of class "ifcm" which is a list with components:

`membership`	a matrix with the membership values of the data points to the clusters.
`hesitation`	a matrix with the hesitation values of the data points to the clusters.
`nonmembership`	a matrix with the nonmembership values of the data points to the clusters.
`centers`	the final cluster centers.
`hardcluster`	the hardclustering results

Erol Egrioglu, Eren Bas and Busenur Kizilaslan
Maintainer: Busenur Kizilaslan busenur.sarica@gmail.com

Chaira, T. (2011) A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images, Applied Soft Computing, Vol. 11(2), 1711-1717, https://www.sciencedirect.com/science/article/pii/S1568494610001067.
Chaira, T., Ray, A. K., Salvetti, O. (2006) Intuitionistic fuzzy C means clustering in medical images, Advances in Pattern Recognition, 226-230, https://www.worldscientific.com/doi/10.1142/9789812772381_0037.