VAT: Visual Assessment of (Cluster) Tendency

In fclust: Fuzzy Clustering

Visual Assessment of (Cluster) Tendency

Description

Digital intensity image to inspect the number of clusters

Usage

 VAT (Xca)

Arguments

Xca	Matrix or data.frame (usually data to be used in the clustering algorithm)

Details

Each cell refers to a dissimilarity between a pair of objects. Small dissimilarities are represented by dark shades and large dissimilarities are represented by light shades. In the plot the dissimilarities are reorganized in such a way that, roughly speaking, (darkly shaded) diagonal blocks correspond to clusters in the data. Therefore, k dark blocks along its main diagonal suggest that the data contain k (as yet unfound) clusters and the size of each block represents the approximate size of the cluster.

Author(s)

Paolo Giordani, Maria Brigida Ferraro, Alessio Serafini

References

Bezdek J.C., Hathaway, R.J., 2002. VAT: a tool for visual assessment of (cluster) tendency. Proceedings of the IEEE International Joint Conference on Neural Networks, , pp. 2225?2230.
Hathaway R.J., Bezdek J.C., 2003. Visual cluster validity for prototype generator clustering models. Pattern Recognition Letters, 24, 1563?1569.
Huband J.M., Bezdek J.C., 2008. VCV2 ? Visual Cluster Validity. In Zurada J.M., Yen G.G., Wang J. (Eds.): Lecture Notes in Computer Science, 5050, pp. 293?308. Springer-Verlag, Berlin Heidelberg.

See Also

plot.fclust, VIFCR, VCV, VCV2, 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]
## data standardization (after removing the column Serving Size)
Mc=scale(Mc[,1:(ncol(Mc)-1)],center=TRUE,scale=TRUE)[,]
## plot of VAT
VAT(Mc)

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