dbscannew.m

using dbscan to clustering in matlab

function [class]=dbscannew(X,k,eps) 
[m,n]=size(X); 
class=zeros(1,m); 
clusterId = 0; 
X=[class',X]; 
 
for i = 1:m 
  clusterId = clusterId + 1; 
  if X(i,1) == 0 
     [X]=expandcluster(i,X,clusterId,eps,k,n); 
  end   
end 
 
class = X(:,1)'; 
 
function [X]=expandcluster(index,X,clusterId,eps,k,n) 
  Di=dist(X(index,(2:n)),X(:,(2:n))); 
  D = find(Di==k)
        for i = 1: length(Result)    
            if X(Result(i),1) == 0|-1 
               if X(Result(i),1)==0 
                  D(length(D)+1)=Result(i); 
               end 
               X(Result(i),1)=clusterId; 
            end; 
         end; 
       end; 
        D(1)=[]; 
     end; 
   end; 
 
   
%function distan = dist(x,y) 
 % sum = 0; 
  %nx = size(x); 
  %for i = 1:nx 
   %   sum = (x(i)-y(i))^2+sum; 
   %end 
 %distan = sqrt(sum)  
  
 %............................................ 
function [Di]=dist(i,x) 
 
% function: [D]=dist(i,x) 
% 
% Aim:  
% Calculates the Euclidean distances between i and all objects in x	  
%								     
% Input:  
% i - an object (1,n) 
% x - data matrix (m,n); m-objects, n-variables	     
%                                                                  
% Output:  
% D - Euclidean distance (m,1) 
 
 
[m,n]=size(x); 
Di=sqrt(sum((((ones(m,1)*i)-x).^2)')); 
 
if n==1 
   Di=abs((ones(m,1)*i-x))'; 
end