Let n be the number of clusters you wantLet S be the set of feature vectors (|S| is the size of the set)Let A be the set of associated clusters for each feature vectorLet sim(x,y) be the similarity functionLet c[n] be the vectors for our clustersInit:   Let S' = S   //choose n random vectors to start our clusters   for i=1 to n      j = rand(|S'|)      c[n] = S'[j]      S' = S' - {c[n]} //remove that vector from S' so we can't choose it again   end   //assign initial clusters   for i=1 to |S|      A = argmax(j = 1 to n) { sim(S, c[j]) }   endRun:   Let change = true   while change     change = false //assume there is no change     //reassign feature vectors to clusters     for i = 1 to |S|        a = argmax(j = 1 to n) { sim(S, c[j]) }        if a != A           A = a           change = true //a vector changed affiliations -- so we need to                          //recompute our cluster vectors and run again        end     end     //recalculate cluster locations if a change occurred     if change        for i = 1 to n           mean, count = 0            for j = 1 to |S|              if A[j] == i                 mean = mean + S[j]                 count = count + 1              end           end           c = mean/count         end     end