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)
x |
a |
... |
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
iVAT.
Michael Hahsler
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))
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.