image,kcca-method

image,kccasimple-method

Index number of dimensions of input space to plot.

which

npoints

If <code>TRUE</code>, a greedy algorithm is used for the
    background colors of the segments, which may result in neighbouring
    segments having the same color. If <code>FALSE</code>, neighbouring
    segments always have different colors at a speed penalty.

fastcol

Vector of background colors for the segments.

Line width of contour lines at cluster boundaries, use
    larger values for <code>npoints</code> than the default to get smooth
    lines. (Warning: We really need a smarter way to draw cluster
    boundaries!)

clwd

Logical, add a neighborhood graph to the plot?

graph

xlab, ylab, ...


  Image plot of cluster segments overlaid by neighbourhood graph.


methods

hplot

image-methods

The main function kcca implements a general framework for
k-centroids cluster analysis supporting arbitrary distance
measures and centroid computation. Further cluster methods
include hard competitive learning, neural gas, and QT
clustering. There are numerous visualization methods for
cluster results (neighborhood graphs, convex cluster hulls,
barcharts of centroids, ...), and bootstrap methods for the
analysis of cluster stability.

image-methods: Methods for Function image in Package `flexclust'

Description

Usage

Arguments

Details

See Also