Code covered by the BSD License
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[AR,RI,MI,HI]=RandIndex(c1,c2...
RANDINDEX - calculates Rand Indices to compare two partitions
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[indx,ssw,sw,sb]=valid_cluste...
clustering validation indices
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daisy(x,vtype,metric)
DAISY returns a matrix containing all the pairwise dissimilarities
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ind2cluster(labels)
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pam(x,kclus,vtype,stdize,metr...
PAM returns a list representing a clustering of the data into kclus
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similarity_euclid(data)
data --- observations x dimensions, every collumn is standardized within [0, 1]
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similarity_euclid(data)
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similarity_pearson(data)
pearson coefficients between every two columns
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similarity_pearsonC(data, C)
pearson coefficients between every column and the center
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valid_errorate(labels, truela...
computing error rates for every clusters if true labels are given
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valid_findk(S, kfind, id, k, ...
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valid_internal_deviation(data...
cluster validity indices based on deviation
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valid_internal_intra(Smatrix,...
indices base on intra and inter similarity
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valid_intrainter(Smatrix,U)
caculate intra similarity/distance and inter similarity
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valid_plotall(validty, ks, B,...
preparing for plotting indices
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valid_sumpearson(data,labels,...
within-, between-cluster and total sum of squares
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valid_sumsqures(data,labels,k...
data: a matrix with each column representing a variable.
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xlim(arg1, arg2)
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mainClusterValidationNC.m
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validity_Index.m
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View all files
from
(simple) Tool for estimating the number of clusters
by Kaijun Wang
12 validity indices, illustrate estimation of the number of clusters
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| similarity_pearson(data)
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function R = similarity_pearson(data)
% pearson coefficients between every two columns
% input matrix: data --- nrow rows * ncol columns
% output matrix: R --- ncol * ncol matrix
[nrow,ncol] = size(data);
x = mean(data);
data = data-repmat(x,nrow,1);
R = ones(ncol,ncol);
for i = 1:ncol-1
x = data(:,i);
X = sqrt(x'*x);
for j = i+1:ncol
y = data(:,j);
xy = x'*y;
Y = sqrt(y'*y);
sim = xy/(X*Y);
R(i,j) = sim;
R(j,i) = sim;
end
end
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