Diana.rar_DIANA算法_层次 聚类_层次聚类diana_聚类算法diana

function result = diana_cluster(x,vtype,stdize,metric,banner) %DIANA is a divisive clustering algorithm. It returns a hierarchy of clusters % % The algorithm is fully described 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) % or Dissimilarity matrix (if number of rows equals 1) % 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 % (if x is a dissimilarity matrix, vtype is not reauired) % % Optional input arguments: % stdize : standardise the variables given by the x-matrix % Possible values are 0 : no standardisation (default) % 1 : standardisation by the mean % 2 : standardisation by the median % (if x is a dissimilarity matrix, stdize is ignored) % metric : Metric used to calculate the dissimilarity matrix % Possible values are 0 : Euclidian (all interval variables, default) % 1 : Manhattan % 2 : Mixed (not all interval variables, default) % (if x is a dissimilarity matrix, metric is ignored) % banner : draws picture % Possible values are 0 : do not create a banner (default) % 1 : create a banner % I/O: % result=diana(x,vtype,metric,stdize,banner) % % Example (subtracted from the referenced book) % load agricul.mat % result=diana(agricul,[4 4],0,0,1); % % The output of DIANA is a structure containing: % result.x : inputmatrix x % result.diss : whether the inputmatrix x is a dissimilarity matrix % or not % result.dys : calculated dissimilarities (read row by row from the % lower dissimilarity matrix, without the elements of % the diagonal) % result.metric : metric used % result.stdize : standardisation used % result.number : number of observations % result.objectorder: order of objects % result.heights : diameter of cluster before deviding it % (=length of banner) % result.dc : divisive coefficient % result.merge : a (n-1) by 2 matrix related to the merge % % % And DIANA will create the plot banner if banner equals 1. % % This function is part of LIBRA: the Matlab Library for Robust Analysis, % available at: % http://wis.kuleuven.be/stat/robust.html % % Written by Wai Yan Kong % Created on 05/2006 % Last Revision: 19/09/2006 res1=[]; %Checking and filling in the inputs if (nargin<1) error('One input argument required (data or dissimilarity matrix)') elseif ((nargin<2) & (size(x,1)~=1)) error('Two input arguments required (datamatrix x and vtype)') % so, only datamatrix x as input elseif (nargin<2) metri ='unknown'; metr='unknown'; stdize = 0; banner = 0; % so, only dissim matrix x as input elseif (nargin<3) stdize = 0; banner = 0; if (sum(vtype)~=4*size(x,2)) metr ='mixed'; metri='mixed'; else metr ='eucli'; metri='euclidean'; end % so, only datamatrix or dissimilarity matrix x and vtype % as input elseif (nargin<4) banner = 0; if (sum(vtype)~=4*size(x,2)) metr ='mixed'; metri='mixed'; else metr ='eucli'; metri='euclidean'; end % so, only datamatrix or dissimilarity matrix x, vtype and % stdize as input elseif (nargin<5) banner = 0; elseif (nargin>5) error('Too many input arguments') end % defining metric (for 4 input arguments) and diss if (nargin>=4) if (metric==0) metr='eucli'; metri='euclidean'; elseif (metric==1) metr='manha'; metri='manhattan'; elseif (metric==2) metr='mixed'; metri='mixed'; else error('metric must be 0,1 or 2') end end if ((size(x,1)~=1)) diss=0; dissi='x is no dissimilarity matrix'; else diss=1; dissi='x is a dissimilarity matrix'; end %Standardization if ((stdize==1) & (metr=='eucli'| metr=='manha') & (diss==0)) x = ((x - repmat(mean(x),size(x,1),1))./(repmat(std(x),size(x,1),1))); standardisation='standardisation by mean'; elseif ((stdize==2) & (metr=='eucli' | metr=='manha') & (diss==0)) x = ((x - repmat(median(x),size(x,1),1))./(repmat(mad(x),size(x,1),1))); standardisation='standardisation by median'; elseif(stdize==0) standardisation='no standardisation'; elseif (stdize==1 | stdize==2) standardisation='no standardisation (not enough num var or x is a diss matrix)'; elseif (nargin<=2) standardisation='no standardisation'; else error('stdize must be 0,1 or 2'); end % defining dissimilarity matrix and number if (diss==1) disv=x; number=(1+sqrt(1+8*size(x,2)))/2; %number of observations % checking for missing values in the dissimilarity matrix if any(isnan(disv)) error('There are missing value(s) in the dissimilarity matrix!') end % checking the dimensions of the dissimilarity matrix if mod(number,fix(number))~=0 error(['The dimension of the dissimilarity matrix is not correct!']) end else resl=daisy(x,vtype,metr); disv=resl.disv; number=size(x,1); end %Actual calculations [ner,ban,coef,merge,dys]=twinsc(number,[0 disv]',1,2); % We want ban to be a vector of length n-1 ban2=zeros(1,(number-1)); for i = 1:(number-1) ban2(i) = ban(i+1); end % We want merge to be a (n-1) by 2 matrix merge2=ones(number-1,2); for i = 1:(number-1) merge2(i,:) = merge(2*i-1:2*i); end % Create a banner if (banner==1) Y=ban2; Y1=fliplr(Y); whitebg([0.4 0.5 0.75]); %set(gcf,'Color',[0.8 0.8 0.8]); b=barh(Y1,1,'w'); title 'Banner of Diana' ; xlabel('Height'); set(gca,'XDir','reverse'); YT=0.5:number; set(gca,'YTick',YT); set(gca,'YTickLabel',fliplr(ner)); axis([min([ban2 0]),max([ban2 0]),0.5,number-0.5]); elseif ((banner~=0) & (banner~=1) & (nargin==5)) error('banner must equals 0 or 1') end %Putting things together result = struct('x',x,'diss',dissi,'dys',dys,'metric',metri,... 'stdize',standardisation,'number',number,... 'objectorder',ner,'heights',ban2,'dc',coef,'merge',merge2); if diss result=rmfield(result, 'x'); end