Plots of a Divisive Hierarchical Clustering

DESCRIPTION:

Creates plots for visualizing a diana object.

USAGE:

plot.diana(x, ask=F, which.plots=NULL, ...)

REQUIRED ARGUMENTS:

x
an object of class "diana", created by the function diana.

OPTIONAL ARGUMENTS:

ask
if TRUE, plot.diana operates in interactive mode.
which.plots
integer vector indicating which plots to display. Graphical parameters may also be supplied as arguments to this function (see ).

VALUE:

a NULL value is returned.

SIDE EFFECTS:

An appropriate plot is produced on the current graphics device. This can be one or both of the following choices:

Banner

Clustering tree

DETAILS:

When ask=T, rather than producing each plot sequentially, plot.diana displays a menu listing all the plots that can be produced. If the menu is not desired but a pause between plots is still wanted one must set par(ask=T) before invoking the plot command.

The banner displays the hierarchy of clusters, and is equivalent to a tree. See Rousseeuw (1986) or chapter 6 of Kaufman and Rousseeuw (1990). The banner plots the diameter of each cluster being splitted. The observations are listed in the order found by the diana algorithm, and the numbers in the height vector are represented as bars between the observations.

The leaves of the clustering tree are the original observations. A branch splits up at the diameter of the cluster being splitted.

NOTE:

In the banner plot, observation labels are only printed when the number of observations is limited to less than 35, for readability. Moreover, observation labels are truncated to at most 5 characters.

REFERENCES:

Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.

Rousseeuw, P. J. (1986). A visual display for hierarchical classification. In Data Analysis and Informatics 4. Edited by E. Diday, Y. Escoufier, L. Lebart, J. Pages, Y. Schektman, and R. Tomassone. North-Holland, Amsterdam. pp. 743-748.

Struyf, A., Hubert, M. and Rousseeuw, P. J. (1997). Integrating robust clustering techniques in S-PLUS. Computational Statistics and Data Analysis, 26, 17-37.

SEE ALSO:

, , , .

EXAMPLES:

plot(diana(votes.repub))