FKM.med: Fuzzy k-medoids

In fclust: Fuzzy Clustering

Fuzzy k-medoids

Description

Performs the fuzzy k-medoids clustering algorithm.
Differently from fuzzy k-means where the cluster prototypes (centroids) are artificial objects computed as weighted means, in the fuzzy k-medoids the cluster prototypes (medoids) are a subset of the observed objects.

Usage

 FKM.med (X, k, m, RS, stand, startU, index, alpha, conv, maxit, seed)

Arguments

X	Matrix or data.frame
k	An integer value or vector indicating the number of clusters (default: 2:6)
m	Parameter of fuzziness (default: 1.5)
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 start for the membership degree matrix U (default: no rational start)
index	Cluster validity index to select the number of clusters: PC (partition coefficient), PE (partition entropy), MPC (modified partition coefficient), SIL (silhouette), SIL.F (fuzzy silhouette), XB (Xie and Beni) (default: "SIL.F")
alpha	Weighting coefficient for the fuzzy silhouette index SIL.F (default: 1)
conv	Convergence criterion (default: 1e-9)
maxit	Maximum number of iterations (default: 1e+6)
seed	Seed value for random number generation (default: NULL)

Details

If startU is given, the argument k is ignored (the number of clusters is ncol(startU)).
If startU is given, the first element of value, cput and iter refer to the rational start.
In FKM.med the parameter of fuzziness is usually lower than the one used in FKM.

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.med)
clus	Matrix containing the indexes of the clusters where the objects are assigned (column 1) and the associated membership degrees (column 2)
medoid	Vector containing the indexes of the medoid objects
value	Vector containing the loss function values for the RS starts
criterion	Vector containing the values of the cluster validity index
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.med)
b	Parameter of the polynomial fuzzifier (NULL for FKM.med)
vp	Volume parameter (NULL for FKM.med)
delta	Noise distance (NULL for FKM.med)
gam	Weighting parameter for the fuzzy covariance matrices (NULL for FKM.med)
mcn	Maximum condition number for the fuzzy covariance matrices (NULL for FKM.med)
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
D	Dissimilarity matrix (NULL for FKM.med)
call	Matched call

Author(s)

Paolo Giordani, Maria Brigida Ferraro, Alessio Serafini

References

Krishnapuram R., Joshi A., Nasraoui O., Yi L., 2001. Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Transactions on Fuzzy Systems, 9, 595-607.

See Also

FKM.med.noise, Fclust, Fclust.index, print.fclust, summary.fclust, plot.fclust, Mc

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-medoids, fixing the number of clusters
## (excluded the factor column Type (last column))
clust=FKM.med(Mc[,1:(ncol(Mc)-1)],k=6,m=1.1,RS=10,stand=1)
## fuzzy k-medoids, selecting the number of clusters
## (excluded the factor column Type (last column))
clust=FKM.med(Mc[,1:(ncol(Mc)-1)],k=2:6,m=1.1,RS=10,stand=1)

## End(Not run)

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