Code covered by the BSD License
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Vcolabel(lb,H,C)
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Vdraw_cline(N, labels, x, y)
Drawing the layout of circles connected with lines
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Vmake_position(Hm,Hmax,elg,c,...
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[A1,A2,A1h,A2h,B1,B2,B1h,B2h]...
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[dmin,dhaus,Ia,Ib,Ha,Hb]=adis...
inter-cluster distances: min distance, Hausdorff distance
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[idx,netsim,dpsim,expref,pref...
[idx,netsim,dpsim,expref,pref]=apclusterK(s,k)
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[idx,netsim,dpsim,expref,pref...
Finds approximately k clusters using affinity propagation (BJ Frey and
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[idx,netsim,dpsim,expref]=apc...
% [idx,netsim,dpsim,expref]=apclusterSparse(s,p)
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[idx,netsim,i,unconverged,dps...
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[pcfirst,eigvect,eigval,pcord...
extracting PCA components from data (along dimensions)
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border_distance(Dmatrix,A1,B1...
finding nearest elements between A1 & B1
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border_points(Dm,A,B,N)
finding N points in group A that are nearest to B
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clusterrun(whichAP,data,K,typ...
finding K clusters exactly
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data_load(sw,alabel,nrow,sima...
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ind2cluster(labels)
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plotdata_label(data,classlabe...
plot the first two principle components (PC1 & PC2) of data with labels
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propag_center(data,Dmatrix, A...
computing propagation distances in centeral area
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propag_intra(Dmatrix, A, star...
compute distances within A along different propagation routes
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propag_through(DM, A, startpo...
compute distances between points along the propagation route
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simatrix_ap(data,type,chois)
data: a matrix with each column representing a variable.
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similarity_euclid(data,vararg...
input: data --- observations x dimensions
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similarity_pearson(data,varar...
pearson coefficients between columns
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test_regionsize.m
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tiedrank2(x, tieflag, bidirec...
TIEDRANK Compute the ranks of a sample, adjusting for ties.
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valid_errorate(labels, truela...
computing error rates for every clusters if true labels are given
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valid_external(index1,c2)
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ylim2(arg1, arg2)
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Main_cRelation.m
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Visual_cRelation_main.m
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View all files
from
Recognizing Far-Near Relations of Clusters by GDEM, Visualization by Line-Pearl Pattern
by Kaijun Wang
measure far-near degrees (distances) between clusters & dense degrees of border regions of clusters
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| ind2cluster(labels)
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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
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