Fclust: Fuzzy clustering

In fclust: Fuzzy Clustering

Fuzzy clustering

Description

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

Usage

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

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)

Details

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

Value

clust

Object of class fclust

Author(s)

Paolo Giordani, Maria Brigida Ferraro, Alessio Serafini

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

## Not run: 
## 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)

## End(Not run)

fclust documentation built on Nov. 16, 2022, 5:10 p.m.