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_euclid(data)
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function [R, dmax]= similarity_euclid(data)
% input: data --- observations x dimensions
% output: R --- nrow * nrow matrix with all the pairwise Euclidean distances
% between nrow observations in the dataset.
nrow = size(data,1);
R=zeros(nrow,nrow);
data = data';
dmax=0;
% distance between two observations
for i=1:nrow-1
x=data(:,i);
for j=i+1:nrow
y=x-data(:,j);
d=y'*y;
d=sqrt(d);
R(i,j) = d;
R(j,i) = d;
if d>dmax
dmax=d;
end
end
end
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