fclust (version 2.1.1.1)

Fclust: Fuzzy clustering

Description

Performs fuzzy clustering by using the algorithms available in the package.

Usage

Fclust (X, k, type, ent, noise, stand, distance)

Value

clust

Object of class fclust

Arguments

X

Matrix or data.frame

k

An integer value specifying the number of clusters (default: 2)

type

Fuzzy clustering algorithm: "standard" (standard algorithms: FKM - type if distance=TRUE, NEFRC - type if if distance=FALSE), "polynomial" (algorithms with the polynomial fuzzifier), "gk" (Gustafson and Kessel - like algorithms), "gkb" (Gustafson, Kessel and Babuska - like algorithms), "medoids" (Medoid - based algorithms) (default: "standard")

ent

If ent=TRUE, the entropy regularization variant of the algorithm is run (default: FALSE)

noise

If noise=TRUE, the noise cluster variant of the algorithm is run (default: FALSE)

stand

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

distance

If distance=TRUE, X is assumed to be a distance/dissimilarity matrix (default: FALSE)

Author

Paolo Giordani, Maria Brigida Ferraro, Alessio Serafini

Details

The clustering algorithms are run by using default options.
To specify different options, use the corresponding function.

See Also

print.fclust, summary.fclust, plot.fclust, FKM, FKM.ent, FKM.gk, FKM.gk.ent, FKM.gkb, FKM.gkb.ent, FKM.med, FKM.pf, FKM.noise, FKM.ent.noise, FKM.gk.noise, FKM.gkb.ent.noise, FKM.gkb.noise, FKM.gk.ent.noise,FKM.med.noise, FKM.pf.noise, NEFRC, NEFRC.noise, Fclust.index, Fclust.compare

Examples

if (FALSE) {
## McDonald's data
data(Mc)
names(Mc)
## data normalization by dividing the nutrition facts by the Serving Size (column 1)
for (j in 2:(ncol(Mc)-1))
Mc[,j]=Mc[,j]/Mc[,1]
## removing the column Serving Size
Mc=Mc[,-1]
## fuzzy k-means
## (excluded the factor column Type (last column))
clust=Fclust(Mc[,1:(ncol(Mc)-1)],k=6,type="standard",ent=FALSE,noise=FALSE,stand=1,distance=FALSE)
## fuzzy k-means with polynomial fuzzifier 
## (excluded the factor column Type (last column))
clust=Fclust(Mc[,1:(ncol(Mc)-1)],k=6,type="polynomial",ent=FALSE,noise=FALSE,stand=1,distance=FALSE)
## fuzzy k-means with entropy regularization
## (excluded the factor column Type (last column))
clust=Fclust(Mc[,1:(ncol(Mc)-1)],k=6,type="standard",ent=TRUE,noise=FALSE,stand=1,distance=FALSE)
## fuzzy k-means with noise cluster
## (excluded the factor column Type (last column))
clust=Fclust(Mc[,1:(ncol(Mc)-1)],k=6,type="standard",ent=FALSE,noise=TRUE,stand=1,distance=FALSE)
}