An Algorithm for Clustering Tendency Assessment

The visual assessment of tendency (VAT) technique, developed by J.C. Bezdek, R.J. Hathaway and J.M. Huband, uses a visual approach to find the number of clusters in data. In this paper, we develop a new algorithm that processes the numeric output of VAT programs, other than gray level images as in VAT, and produces the tendency curves. Possible cluster borders will be seen as high-low patterns on the curves, which can be caught not only by human eyes but also by the computer. Our numerical results are very promising. The program caught cluster structures even in cases where the visual outputs of VAT are virtually useless. Key–Words: Clustering, similarity measures, data visualization, clustering tendency