JACCARD.F: Fuzzy Jaccard index

In fclust: Fuzzy Clustering

Fuzzy Jaccard index

Description

Produces the fuzzy version of the Jaccard index between a hard (reference) partition and a fuzzy partition.

Usage

JACCARD.F(VC, U, t_norm)

Arguments

VC	Vector of class labels
U	Fuzzy membership degree matrix or data.frame
t_norm	Type of the triangular norm: "minimum" (minimum triangular norm), "triangular product" (product norm) (default: "minimum")

Value

jaccard.fValue of the fuzzy Jaccard index

Author(s)

Paolo Giordani, Maria Brigida Ferraro, Alessio Serafini

References

Campello, R.J., 2007. A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recognition Letters, 28, 833-841.
Jaccard, P., 1901. Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, 37, 547-579.

See Also

ARI.F, RI.F, 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=FKM(Mc[,1:(ncol(Mc)-1)],k=6,m=1.5,stand=1)
## fuzzy Jaccard index
jaccard.f=JACCARD.F(VC=Mc$Type,U=clust$U)

## End(Not run)

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