cluster (version 1.3-2)

clusplot.default: Bivariate Cluster Plot (clusplot)

Description

Creates a bivariate plot visualizing a partition (clustering) of the data. All observation are represented by points in the plot, using principal components or multidimensional scaling. Around each cluster an ellipse is drawn.

Usage

clusplot.default(x, clus, diss = FALSE, cor = TRUE, stand = FALSE,
                 lines = 2, shade = FALSE, color = FALSE,
                 labels= 0, plotchar = TRUE,
                 col.p = "dark green", col.txt = col.p,
                 span = TRUE, xlim = NULL, ylim = NULL, ...)

Arguments

x

matrix or dataframe, or dissimilarity matrix, depending on the value of the diss argument.

In case of a matrix (alike), each row corresponds to an observation, and each column corresponds to a variable. All variables must be

clus

a vector of length n representing a clustering of x. For each observation the vector lists the number or name of the cluster to which it has been assigned. clus is often the clustering component of the output of

diss

logical indicating if x will be considered as a dissimilarity matrix or a matrix of observations by variables (see x arugment above).

cor

logical flag, only used when working with a data matrix (diss = FALSE). If TRUE, then the variables are scaled to unit variance.

stand

logical flag: if true, then the representations of the n observations in the 2-dimensional plot are standardized.

lines

integer out of 0, 1, 2, used to obtain an idea of the distances between ellipses. The distance between two ellipses E1 and E2 is measured along the line connecting the centers $m1$ and $m2$ of the two ellipses.

In case E1 a

shade

logical flag: if TRUE, then the ellipses are shaded in relation to their density. The density is the number of points in the cluster divided by the area of the ellipse.

color

logical flag: if TRUE, then the ellipses are colored with respect to their density. With increasing density, the colors are light blue, light green, red and purple. To see these colors on the graphics device, an appropriate color scheme shoul

labels

integer code, currently one of 0,1,2,3 and 4. If [object Object],[object Object],[object Object],[object Object],[object Object] The levels of the vector clus are taken as labels for the clusters. The labels of the points a

plotchar

logical flag: if TRUE, then the plotting symbols differ for points belonging to different clusters.

span

logical flag: if TRUE, then each cluster is represented by the ellipse with smallest area containing all its points. (This is a special case of the minimum volume ellipsoid.) If FALSE, the ellipse is based on the mean and covariance matrix of the

col.p

color code used for the observation points.

col.txt

color code for used for the labels.

xlim, ylim

length 2 vectors giving the x- and y- ranges as in plot.default.

...

Further graphical parameters may also be supplied, see par.

Value

• An invisible list with components:
• Distances
  When lines is 1 or 2 we optain a k by k matrix (k is the number of clusters). The element in [i,j] is the distance between ellipse i and ellipse j. If lines = 0, then the value of this component is NA.
• Shading
  A vector of length k (where k is the number of clusters), containing the amount of shading per cluster. Let y be a vector where element i is the ratio between the number of points in cluster i and the area of ellipse i. When the cluster i is a line segment, y[i] and the density of the cluster are set to NA. Let z be the sum of all the elements of y without the NAs. Then we put shading = y/z *37 + 3 .

Side Effects

a visual display of the clustering is plotted on the current graphics device.

Details

clusplot uses the functions princomp and cmdscale. These functions are data reduction techniques. They will represent the data in a bivariate plot. Ellipses are then drawn to indicate the clusters. The further layout of the plot is determined by the optional arguments.

References

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

Pison, G., Struyf, A. and Rousseeuw, P.J. (1997). Displaying a Clustering with CLUSPLOT, Technical Report, University of Antwerp, submitted.

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

princomp, cmdscale, pam, clara, daisy, par, identify, cov.mve, clusplot.partition.

Examples

## plotting votes.diss(dissimilarity) in a bivariate plot and
## partitioning into 2 clusters
data(votes.repub)
votes.diss <- daisy(votes.repub)
votes.clus <- pam(votes.diss, 2, diss = TRUE)$clustering
clusplot(votes.diss, votes.clus, diss = TRUE, shade = TRUE)

if(interactive()) #  uses identify() *interactively* :
  clusplot(votes.diss, votes.clus, diss = TRUE, shade = TRUE,
           labels = 1)

## plotting iris (dataframe) in a 2-dimensional plot and partitioning
## into 3 clusters.
data(iris)
iris.x <- iris[, 1:4]
clusplot(iris.x, pam(iris.x, 3)$clustering, diss = FALSE,
         plotchar = TRUE, color = TRUE)