Useful visualization of a distance matrix of clustered points. D is sorted into k blocks, where the ith block contains all the points in cluster i. When D is displayed the blocks are shown explicitly. Hence for a good clustering (under a spherical gaussian assumption) the 'diagonal' blocks ought to be mostly dark, and all other block ought to be relatively white. One can thus quickly visualize the quality of the clustering, or even how clusterable the points are. Outliers (according to IDX) are removed from D. USAGE [D, Dsm] = distMatrixShow( D, IDX, [show] ) INPUTS D - nxn distance matrix IDX - cluster membership [see kmeans2.m] show - [1] will display results in figure(show) OUTPUTS D - sorted nxn distance matrix Dsm - sorted and smoothed nxn distance matrix EXAMPLE % not the best example since points are already ordered [X,IDX] = demoGenData(100,0,5,2,10,2,0); distMatrixShow( pdist2(X,X), IDX ); See also VISUALIZEDATA, KMEANS2 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]