zcebeci/VatAna: Visual Assessment of Clustering Tendency for Finding the Number of Clusters in a Dataset

The partitioning algorithms require a priori estimate of number of clusters (k) as an input parameter, and thus the success of partitioning depends mostly on this parameter. In order to find an optimal estimation of k, the obtained results are checked by the cluster validity indices at the end of each run of successive cluster analyses. Unfortunately, this kind of cluster validation is time consuming task, and also depends on the clustering indices which may not guarantee the quality of clustering since their performances vary with complexity in data structures. In order to find an optimal number of clusters in datasets, one can benefit from the preprocessing approaches like visual assessment of clustering tendency algorithm before going to clustering session. The visual assessment of clustering tendency (VAT) is a frontier algorithm which produces a grey-level image of the reordered distance matrix showing existing clusters with dark blocks along its diagonal. This R package provides various functions related with VAT analysis and demonstrates its usage with the examples.

install.packages("remotes")
remotes::install_github("zcebeci/VatAna")