VAT function

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, 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, ...)

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.

Returns

Nothing.

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.

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))

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()

Author(s)

Michael Hahsler