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 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]

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