Tendency Curves for Visual Clustering Assessment
Proceedings of the WSEAS International Conference on Applied Computing Conference
We improve the visual assessment of tendency (VAT) technique, which, developed by J.C.
Bezdek, R.J. Hathaway and J.M. Huband, uses a visual approach to find the number of clusters in data. Instead of using square gray level images of dissimilarity matrices as in VAT, we further process the matrices and produce the tendency curves. Possible cluster structure will be shown as peak-valley patterns on the curves, which can be caught not only by human eyes but also by the computer. Our numerical experiments showed that the computer can catch cluster structures from the tendency curves even in cases where the visual outputs of VAT are virtually useless.
Hu, Yingkang, Richard J. Hathaway.
"Tendency Curves for Visual Clustering Assessment."
Proceedings of the WSEAS International Conference on Applied Computing Conference, M. Demiralp, W. Mikhael, A. Caballero, N. Abatzoglou, M. Tabrizi, R. Leandre, M. Garcia-Planas and R. Choras (Ed.): 274-279 Stevens Point, Wisconsin: Mathematics and Computers in Science and Engineering. World Scientific and Engineering Academy and Society (WSEAS).
source: http://www.wseas.us/e-library/conferences/2008/istanbul/acc/acc_46.pdf isbn: 960-6766-67-1