# visualizeData

## PURPOSE

Project high dim. data unto principal components (PCA) for visualization.

## SYNOPSIS

function visualizeData( X, k, IDX, types, C )

## DESCRIPTION

     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 Image&Video Toolbox      Version 2.0
     Copyright 2008 Piotr Dollar.  [pdollar-at-caltech.edu]
     Please email me if you find bugs, or have suggestions or questions!
     Licensed under the Lesser GPL [see external/lgpl.txt]

## CROSS-REFERENCE INFORMATION

This function calls:

- pca Principal components analysis (alternative to princomp).
- pcaApply Companion function to pca.

This function is called by:

- demoCluster Clustering demo.