Digital intensity image generated using the membership degree matrix to do cluster validation. The function also plots the VAT image.
Matrix or data.frame (usually data used in the clustering algorithm)
Membership degree matrix
If a subset of the plots is required, specify a subset of the numbers
Plot 1 (
which=1): VAT. 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.
Plot 2 (
which=2): VCV2. Each cell refers to a dissimilarity between a pair of objects computed with respect to the cluster membership degrees. Small dissimilarities are represented by dark shades and large dissimilarities are represented by light shades. In the plot the dissimilarities are reorganized by using the VAT reordering. If k dark blocks along its main diagonal are visible, then a k-cluster structure is revealed. Note that the actual number of clusters can be revealed even when a larger number of clusters is used. This suggests that the correct value of k can sometimes be found by running the algorithm with a large value of k, and then ascertaining its correct value from the visual evidence in the VCV2 image.
Paolo Giordani, Maria Brigida Ferraro, Alessio Serafini
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.
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.
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## 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) ## plots of VAT and VCV2 VCV2(clust$Xca,clust$U) ## plot of VCV2 VCV2(clust$Xca,clust$U, 2)
 "Serving Size" "Calories" "Total Fat (g)"  "Saturated Fat (g)" "Trans Fat (g)" "Cholesterol (mg)"  "Sodium (mg)" "Carbohydrates (g)" "Dietary Fiber (g)"  "Sugars (g)" "Protein (g)" "Vitamin A (%DV)"  "Vitamin C (%DV)" "Calcium (%DV)" "Iron (%DV)"  "Type"
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