Project high dim. data unto principal components (PCA) for visualization.
Optionally IDX can be specified to indicate different classes for the
points; in this case points in different classes are displayed using
different colors. Up to 12 types are handled (for technical reasons
involving plot), any cluster with a label>12 is assigned the label 12.
USAGE
visualizeData( X, k, [IDX], [types], [C] )
INPUTS
X - column vector of data - N vectors of dimension p (X is Nxp)
k - dimension to which to reduce data (2 or 3)
IDX - [] cluster membership [see kmeans2.m]
types - [] cell array of length ntypes of text labels for each type
C - [] cluster centers (Kxp)
OUTPUTS
EXAMPLE
X = [randn(100,5); randn(100,5)+4];
C = [mean(X(1:100,:)); mean(X(101:200,:))];
IDX = [ones(100,1); 2*ones(100,1)];
visualizeData( X, 2, IDX, {'type1','type2' }, C);
See also KMEANS2, DEMOCLUSTER
Piotr's Computer Vision Matlab Toolbox Version 2.0
Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
Licensed under the Simplified BSD License [see external/bsd.txt]