Butterfly data
Synthetic dataset with 2 clusters and some outliers.
data(butterfly)
A matrix with 17 rows and 2 columns.
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.
Paolo Giordani, Maria Brigida Ferraro, Alessio Serafini
## 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)
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