data = [0 1 3 4; 0 1 4 1]; % original pattern with local minimum
M = 100; % number of times pattern is replicated
rand('state',1); % run deterministically (to exactly duplicate results)

% replicate pattern M times
x=100*rand(M,1)-50; y=100*rand(M,1)-50;
dataorig=data; data=[]; for m=1:length(x), data=[data, [dataorig(1,:)+x(m); dataorig(2,:)+y(m)]]; end;
plot(data(1,:),data(2,:),'ro'); xlabel('x'); ylabel('y');

% compute similarity matrix
for i=1:length(data), for j=1:length(data), S(i,j)=sum(-(data(:,i)-data(:,j)).^2); end; end;

pref=-10;
[ap.idx,ap.netsim,ap.dpsim,ap.expref] = apcluster(S,pref,'plot','details');
k=length(unique(ap.idx(:,end)));
[vsh.idx,vsh.netsim,vsh.dpsim,vsh.expref] = vshcluster(S,k,'details','preference',pref);