(simple) Tool for estimating the number of clusters: similarity_euclid(data) - File Exchange

Code covered by the BSD License

Highlights from
(simple) Tool for estimating the number of clusters

[AR,RI,MI,HI]=RandIndex(c1,c2... RANDINDEX - calculates Rand Indices to compare two partitions
[indx,ssw,sw,sb]=valid_cluste... clustering validation indices
daisy(x,vtype,metric) DAISY returns a matrix containing all the pairwise dissimilarities
ind2cluster(labels)
pam(x,kclus,vtype,stdize,metr... PAM returns a list representing a clustering of the data into kclus
similarity_euclid(data) data --- observations x dimensions, every collumn is standardized within [0, 1]
similarity_euclid(data)
similarity_pearson(data) pearson coefficients between every two columns
similarity_pearsonC(data, C) pearson coefficients between every column and the center
valid_errorate(labels, truela... computing error rates for every clusters if true labels are given
valid_findk(S, kfind, id, k, ...
valid_internal_deviation(data... cluster validity indices based on deviation
valid_internal_intra(Smatrix,... indices base on intra and inter similarity
valid_intrainter(Smatrix,U) caculate intra similarity/distance and inter similarity
valid_plotall(validty, ks, B,... preparing for plotting indices
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.
xlim(arg1, arg2)
mainClusterValidationNC.m
validity_Index.m
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from (simple) Tool for estimating the number of clusters by Kaijun Wang
12 validity indices, illustrate estimation of the number of clusters

similarity_euclid(data)

function data = similarity_euclid(data)
% data --- observations x dimensions, every collumn is standardized within [0, 1]

   nrow = size(data,1);
   colmin = min(data);
   colmax = max(data);
   dmax = colmax-colmin;
   data = data - repmat(colmin,nrow,1);
   data = data./repmat(dmax,nrow,1);

Highlights from (simple) Tool for estimating the number of clusters

Highlights from
(simple) Tool for estimating the number of clusters