Visual Analysis for Cluster Tendency Assessment (VAT/iVAT)
Implements Visual Analysis for Cluster Tendency Assessment (VAT; Bezdek and Hathaway, 2002) and Improved Visual Analysis for Cluster Tendency Assessment (iVAT; Wang et al, 2010).
VAT(x, ...) iVAT(x, ...) path_dist(x)
- further arguments are passed on to
path_dist redefines the distance between two objects as the minimum over
the largest distances in all possible paths between the objects as used for
Bezdek, J.C. and Hathaway, R.J. (2002): VAT: a tool for visual assessment of (cluster) tendency. Proceedings of the 2002 International Joint Conference on Neural Networks (IJCNN '02), Volume: 3, 2225--2230.
Havens, T.C. and Bezdek, J.C. (2012): An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm, IEEE Transactions on Knowledge and Data Engineering, 24(5), 813--822.
Wang L., U.T.V. Nguyen, J.C. Bezdek, C.A. Leckie and K. Ramamohanarao (2010): iVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment, Proceedings of the PAKDD 2010, Part I, LNAI 6118, 16--27.
## lines data set from Havens and Bezdek (2011) x <- create_lines_data(250) plot(x, xlim=c(-5,5), ylim=c(-3,3), cex=.2) d <- dist(x) ## create regular VAT VAT(d, colorkey = TRUE, main = "VAT") ## same as: pimage(d, seriate(d, "VAT")) ## create iVAT which shows visually the three lines iVAT(d, main = "iVAT") ## same as: ## d_path <- path_dist(d) ## pimage(d_path, seriate(d_path, "VAT")) ## compare with dissplot (shows banded structures and relationship between ## center line and the two outer lines) dissplot(d, method="OLO_single", main = "Dissplot", col = bluered(100, bias = .5)) ## compare with optimally reordered heatmap hmap(d, method="OLO_single", main = "Heat map (opt. leaf ordering)", col = bluered(100, bias = .5))