seriation (version 1.5.7)

VAT: Visual Analysis for Cluster Tendency Assessment (VAT/iVAT)

Description

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).

Usage

VAT(x, upper_tri = TRUE, lower_tri = TRUE, ...)
iVAT(x, upper_tri = TRUE, lower_tri = TRUE, ...)
path_dist(x)
ggVAT(x, upper_tri = TRUE, lower_tri = TRUE, ...)
ggiVAT(x, upper_tri = TRUE, lower_tri = TRUE, ...)

Value

Nothing.

Arguments

x

a dist object.

upper_tri, lower_tri

a logical indicating whether to show the upper or lower triangle of the VAT matrix.

...

further arguments are passed on to pimage for the regular plots and ggpimage for the ggplot2 plots.

Author

Michael Hahsler

Details

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.

References

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.

See Also

Other plots: bertinplot(), dissplot(), hmap(), palette(), pimage()

Examples

## 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, main = "VAT for Lines")
## same as: pimage(d, seriate(d, "VAT"))

## ggplot2 version
if (require("ggplot2")) {
  ggVAT(d) + labs(title = "VAT")
}

## create iVAT which shows visually the three lines
iVAT(d, main = "iVAT for Lines")
## same as:
## d_path <- path_dist(d)
## pimage(d_path, seriate(d_path, "VAT for Lines"))

## ggplot2 version
if (require("ggplot2")) {
  ggiVAT(d) + labs(title = "iVAT for Lines")
}

## compare with dissplot (shows banded structures and relationship between
## center line and the two outer lines)
dissplot(d, method = "OLO_single", main = "Dissplot for Lines", col = bluered(100, bias = .5))

## compare with optimally reordered heatmap
hmap(d, method = "OLO_single", main = "Heatmap for Lines (opt. leaf ordering)",
  col = bluered(100, bias = .5))