ClusterUsingCorr

 [results] = ClusterUsingCorr(data,distance)

 ClusterUsingCorr:     Orders a set of features (or variables) according to their correlation
                       Strong correlated variables are intended to be clustered together
                       Affinity propagation is used to cluster the
                       features. Accordingly, the exemplars are also given as an output.
                       Ordering may help to reduce cluttering in parallel coordinates displaying, improving
                       visualization.
 INPUT
 data: A vector of data were rows are observations and columns are
 features
 OUTPUT
 results{1} =  A new ordering of the features, were subsequent features
 are suppossed to be more related
 results{2} =  Original clustering (idx) given by the affinity propagation
 algorithm

 Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)

0001 function[results] = ClusterUsingCorr(data,distance)
0002 % [results] = ClusterUsingCorr(data,distance)
0003 %
0004 % ClusterUsingCorr:     Orders a set of features (or variables) according to their correlation
0005 %                       Strong correlated variables are intended to be clustered together
0006 %                       Affinity propagation is used to cluster the
0007 %                       features. Accordingly, the exemplars are also given as an output.
0008 %                       Ordering may help to reduce cluttering in parallel coordinates displaying, improving
0009 %                       visualization.
0010 % INPUT
0011 % data: A vector of data were rows are observations and columns are
0012 % features
0013 % OUTPUT
0014 % results{1} =  A new ordering of the features, were subsequent features
0015 % are suppossed to be more related
0016 % results{2} =  Original clustering (idx) given by the affinity propagation
0017 % algorithm
0018 %
0019 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)
0020 
0021 nedges = size(data,2)
0022 y = pdist(data', distance);
0023 rho = max(max(y)) - squareform(y); %Affinity propagation maximizes similarity = minimizes distance
0024 
0025 s = mean(rho)     % The self-similarity measure is the mean of correlations for each variable
0026  
0027 [idx,netsim,dpsim,expref]=apcluster(rho,s); % affinity propagation is done to identify clusters of correlated variables
0028 
0029 [val,index] = sort(idx) % The variables are ordered according to their exemplar and the new ordering is output
0030 
0031 results{1} = index';
0032 results{2} = idx;
0033 
0034 return