Adaptive Affinity Propagation clustering: ind2cluster(labels) - File Exchange

Code covered by the BSD License

Highlights from
Adaptive Affinity Propagation clustering

... Handle arguments to function
[idx,netsim,i,unconverged,dps...
ind2cluster(labels)
matrix_transform(M,ngiven,nma... transform to full matrix
silhouette2(X, clust, distanc... SILHOUETTE Silhouette plot for clustered data.
simatrix_make(data,type,nrow) data: a matrix with each column representing a variable.
similarity_euclid(data,vararg...
similarity_pearson(data) pearson coefficients between every two columns
similarity_pearsonC(data, C) pearson coefficients between every column and the center
solution_evaluation(data,M,la...
solution_positive(refseq_exon... load GeneFindingProblem.mat;
valid_errorate(labels, truela... computing error rates for every clusters if true labels are given
valid_external(index1,c2)
valid_sumpearson(data,labels,... within-, between-cluster and total sum of squares
valid_sumsqures(data,labels,k... data: a matrix with each column representing a variable.
Main_adaptAP_demo.m
data_load.m
solution_findK.m
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from Adaptive Affinity Propagation clustering by Kaijun Wang
advantage of speed & performance appears under large number of clusters & large dataset

ind2cluster(labels)

function [clusters, newlabels, Clength] = ind2cluster(labels)

C = unique(labels);
newlabels = labels;
k = length(C);
clusters = cell(1,k);
Clength = zeros(1,k);

for i = 1:k
  ind = find(labels==C(i));
  clusters{i} = ind;
  newlabels(ind) = i;
  Clength(i) = length(ind);
end

Highlights from Adaptive Affinity Propagation clustering

Highlights from
Adaptive Affinity Propagation clustering