vatimage: Generate ordered dissimilarities matrix for VAT analysis
In zcebeci/VatAna: Visual Assessment of Clustering Tendency for Finding the Number of Clusters in a Dataset

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/vatimage.R

This function generates the ordered dissimilarity matrix (ODM) for VAT analysis. ODM can be also displayed as an ordered dissimilarity image (ODI).

1	vatimage(x, normalize=FALSE, disp=TRUE)

`x`	A numeric matrix containing the data.
`normalize`	A logical value whether the the ordered dissimilarities matrix will be normalized or not. The default is FALSE.
`disp`	A logical value whether the the ordered dissimilarities image will be displayed or not. The default is TRUE.

The function works as below:

1.Compute the dissimilarity (DM) matrix between the objects in the examined dataset using the Euclidean distance measure.
2.Reorder the DM for arranging the similar objects are close to each other. The obtained matrix form ordering operation is called as the ordered dissimilarities matrix (ODM).
3.If required the ODM can be visually inspected as the image (ODI).
4.Computed ODM and DM are returned as a list for further analysis in order to count the rectangular block located on the diagonal of ODM.

`odm`	Ordered dissimilarities matrix
`dm`	Original dissimilarities matrix

Zeynel Cebeci

Cebeci, Z. & Yildiz, F. (2015). Gorsel Kumelenme Egilimi Degerlendirmesi ve R'de Uygulamasi. Cukurova Universitesi Ziraat Fakultesi Dergisi, 30 (2), 1-8. (URL: https://dergipark.org.tr/en/download/article-file/219860)

binimage, findk, findk2, genbinimg, greyimage, vatdisp

# Load the dataset protcarbo
data(protcarbo)

# Run vatimage to build ordered dissimilarities matrix 
vatmat <- vatimage(protcarbo, disp=TRUE)$odm

# Find the number of clusters
k <- findk(vatmat)$k
print(k)