function result = daisy(x,vtype,metric)
%DAISY returns a matrix containing all the pairwise dissimilarities
% (distances) between observations in the dataset. The original
% variables may be of mixed types.
%
% The calculation of dissimilarities is explained in:
% Kaufman, L. and Rousseeuw, P.J. (1990),
% "Finding groups in data: An introduction to cluster analysis",
% Wiley-Interscience: New York (Series in Applied Probability and
% Statistics), ISBN 0-471-87876-6.
%
% Required input arguments:
% x : Data matrix (rows = observations, columns = variables)
% vtype : Variable type vector (length equals number of variables)
% Possible values are 1 Asymmetric binary variable (0/1)
% 2 Nominal variable (includes symmetric binary)
% 3 Ordinal variable
% 4 Interval variable
%
% Optional input arguments:
% metric : Metric to be used
% Possible values are 'eucli' Euclidian (all interval variables, default)
% 'manha' Manhattan
% 'mixed' Mixed (not all interval variables, default)
%
% I/O:
% result=daisy(x,vtype,'eucli')
%
% Example (subtracted from the referenced book)
% load flower.mat
% result=daisy(flower,[2 2 1 2 3 3 4]);
%
% The output of DAISY is a structure containing:
% result.disv : dissimilarities (read row by row from the
% lower dissimilarity matrix)
% result.metric : metric used
% result.number : number of observations
%
% This function is part of LIBRA: the Matlab Library for Robust Analysis,
% available at:
% http://wis.kuleuven.be/stat/robust.html
%
% Written by Guy Brys and Wai Yan Kong (May 2006)
%Checking and filling in the inputs
if (nargin<2)
error('Two input arguments required')
elseif (nargin<3)
if (sum(vtype)~=4*size(x,2))
metric = 'mixed';
metr = 0;
else
metric = 'eucli';
metr = 1;
end
elseif (nargin==3)
if strcmp(metric,'eucli')
metr=1;
elseif strcmp(metric,'manha')
metr=2;
elseif strcmp(metric,'mixed')
metr=0;
end
end
%Standardizing in case of mixed metric
if (sum(vtype)~=4*size(x,2))
colmin = min(x);
colextr = max(x)-colmin;
x = (x - repmat(colmin,size(x,1),1))./repmat(colextr,size(x,1),1);
end
%Replacement of missing values
jtmd = repmat(1,1,size(x,2))-2*(sum(isnan(x))>0);
valmisdat = min(min(x))-0.5;
x(isnan(x)) = valmisdat;
valmd = repmat(valmisdat,1,size(x,2));
%Actual calculations
disv=daisyc(size(x,1),size(x,2),x,valmd,jtmd,vtype,metr);
%Putting things together
result = struct('disv',disv,'metric',metric,'number',size(x,1));