butterfly: Butterfly data

In fclust: Fuzzy Clustering

Butterfly data

Description

Synthetic dataset with 2 clusters and some outliers.

Usage

data(butterfly)

Format

A matrix with 17 rows and 2 columns.

Details

The butterfly data motivate the need for the fuzzy approach to clustering.
The presence of outliers can be handled using fuzzy k-means with noise cluster. In fact, differently from fuzzy k-means, the membership degrees of the outliers are low for all the clusters.

Author(s)

Paolo Giordani, Maria Brigida Ferraro, Alessio Serafini

See Also

Fclust, FKM, FKM.noise

Examples

## butterfly data
data(butterfly)
plot(butterfly,type='n')
text(butterfly[,1],butterfly[,2],labels=rownames(butterfly),cex=0.7,lwd=2)
## membership degree matrix using fuzzy k-means (rounded)
round(FKM(butterfly)$U,2)
## membership degree matrix using fuzzy k-means with noise cluster (rounded)
round(FKM.noise(butterfly,delta=3)$U,2)

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