fclust (version 2.1.1.1)

PE: 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)

Value

pe

Value of the partition entropy index

Arguments

U

Membership degree matrix

b

Logarithmic base (default: exp(1))

Author

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)