fclust (version 1.0.1)

FKM: Fuzzy k-means

Description

Performs the fuzzy k-means clustering algorithm.

Usage

FKM (X, k, m, RS, stand, startU, conv, maxit)

Arguments

X

Matrix or data.frame

k

Number of clusters (default: 2)

m

Parameter of fuzziness (default: 2)

RS

Number of (random) starts (default: 1)

stand

Standardization: if stand=1, the clustering algorithm is run using standardized data (default: no standardization)

startU

Rational starting point for the membership degree matrix U (default: no rational start)

conv

Convergence criterion (default: 1e-9)

maxit

Maximum number of iterations (default: 1e+6)

Value

• Object of class fclust, which is a list with the following components:
• U
  Membership degree matrix
• H
  Prototype matrix
• F
  Array containing the covariance matrices of all the clusters (NULL for FKM)
• clus
  Matrix containing the indices of the clusters where the objects are assigned (column 1) and the associated membership degrees (column 2)
• medoid
  Vector containing the indices of the medoid objects (NULL for FKM)
• value
  Vector containing the loss function values for the RS starts
• cput
  Vector containing the computational times (user times) for the RS starts
• iter
  Vector containing the numbers of iterations for the RS starts
• k
  Number of clusters
• m
  Parameter of fuzziness
• ent
  Degree of fuzzy entropy (NULL for FKM)
• vp
  Volume parameter (NULL for FKM)
• delta
  Noise distance (NULL for FKM)
• stand
  Standardization (Yes if stand=1, No if stand=0)
• Xca
  Data used in the clustering algorithm (standardized data if stand=1)
• X
  Raw data
• call
  Matched call

References

Bezdek J.C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, NewYork.

See Also

Fclust, Fclust.index

Examples

data(Mc)
for (j in 2:(ncol(Mc)-1))
Mc[,j]=Mc[,j]/Mc[,1]
Mc=Mc[,-1]
clust=FKM(Mc[,1:(ncol(Mc)-1)],k=6,m=1.5,stand=1)