PE: Partition entropy

In fclust: Fuzzy Clustering

Partition entropy

Description

Produces the partition entropy index. The optimal number of clusters k is is such that the index takes the minimum value.

Usage

 PE (U, b)

Arguments

U	Membership degree matrix
b	Logarithmic base (default: exp(1))

Value

pe	Value of the partition entropy index

Author(s)

Paolo Giordani, Maria Brigida Ferraro, Alessio Serafini

References

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

See Also

PC, MPC, SIL, SIL.F, XB, Fclust, Mc

Examples

## 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=FKM(Mc[,1:(ncol(Mc)-1)],k=6,m=1.5,stand=1)
## partition entropy index
pe=PE(clust$U)

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