fuzzy

I have dataset of 4026*62rows and column and i have to do fuzzification "Accurate Cancer Classification Using Expressions of Very Few Genes"i.e I have to convert all thosevalues into three linguistic values low(L).average(A),high(L),I have coded but my program hangs on wen i run it,i have posted code ,please can anyone help,I need output as A,H,L, for 4026*62 rows and column % 1) FNN classifier: Full lymphoma dataset gene values should be converted into fuzzy values. Using the below code.

 Fuzzification
  [nu da]=xlsread('lymphoma dataset.xls')
  cl=xlsread('class.xls');
  for i=1:size(da,1)
      for j=2:size(da,2)
           if(j<size(da,2))
%            x=str2num(da{i,j});
%          if(isempty(x))
%            x=0;
%            end
          else
               c=unique(da(:,end));
 %               for i1=1:length(c)
 %                   if(strcmp(c{i1},da{i,j}))
 %                       x=i1;
 %                   end
 %               end
 %           end
          data(i,j-1)=x;
      end
   end
  data1=data;
 %  data=data(1:300,:);
 % cl=cl(1:150,:);
 % data=nu;
  disp('Dataset')
  disp(data)
  data=knnimpute(da)
  [in claq]=kmeans(data,4);
 for i=1:size(da,2)
     d=data(:,i);
   Emin=min(d);
     Emax=max(d);
     d1=sort(d,'ascend');
    s=size(d,1)/3;
     pj1=d(1:s,:);
    pj2=d(s+1:end,:);
      cl=in;
    cla1=cl(1:s,:);
     cla2=cl(s+1:end,:);
   nc=10;
     de=1;
    k1 = mat2cell(pj1, nc*ones(1, (s/nc)),1);
     k2 = mat2cell(pj2, nc*ones(1, (size(pj2,1)/nc)),1);
     Rk1=(2*1)/length(pj1);
     Rk2=(2*2)/length(pj2);
    %class
    figure('visible','off')
     h = cdfplot(cla1);
     y = get(h,'YData');
     f1=length(y);
    y = unique(y);     
    h1 = cdfplot(cla2);
     y1 = get(h1,'YData');
     f2=length(y1);
     y1 = unique(y1);
     low1=y(2);
   low2=y1(2);     
   cf1=y(2)-y(1);
     cf2=y1(2)-y1(1);
     Pj1=low1+((Rk1-cf1)/f1)*de;
    Pj2=low2+((Rk2-cf2)/f2)*de;
     Aj1(i)=(Emin+ Pj1)/2;
    Aj2(i) =(Pj1 + Pj2 )/2;
     Aj3(i)= (Pj2 + Emax )/2;   

 end
 c=unique(cl);
 u1=[];
 t = waitbar(0,'Applying Fuzzification  ');
 for i=1:size(data,1)
     waitbar(i/size(data,1))
     for j=1:size(data,2)-1
       if(data(i,j)<=Aj1(j))
            if(data(i,j)> Aj1(j) &&  data(i,j)<Aj2(j))
   U(i,j)=(Aj2(j)-data(i,j))/(Aj2(j)-Aj1(j));
        elseif(data(i,j)<Aj1(j))
           U(i,j)=1;
        else
              U(i,j)=0;
          end
           u(i,j)=1;
            u1{i,j}='L';
         elseif(data(i,j)>=Aj1(j) && data(i,j)<=Aj3(j))
         if(data(i,j)<= Aj1(j))
          U(i,j)=0;
           elseif(data(i,j)> Aj1(j) &&  data(i,j)<Aj2(j))
             U(i,j)=(data(i,j)-Aj2(j))/(Aj2(j)-Aj1(j));
            elseif(data(i,j)==Aj2(j))
                U(i,j)=1;
            elseif(data(i,j)> Aj2(j) &&  data(i,j)<Aj3(j))
          U(i,j)=(Aj3(j)-data(i,j))/(Aj3(j)-Aj2(j));
           else
              U(i,j)=0;

          end   
           u1{i,j}='A';
           u(i,j)=2;
        elseif(data(i,j)>=Aj3(j))
           if(data(i,j)<= Aj2(j))
         U(i,j)=0;
          elseif(data(i,j)> Aj2(j) &&  data(i,j)<Aj3(j))
               U(i,j)=(data(i,j)-Aj2(j))/(Aj3(j)-Aj2(j));
            else
              U(i,j)=1;
            end
           u1{i,j}='H';
           u(i,j)=3;
       end

    end
 end
 close(t);
 U(:,end+1)=cl;
 disp('Fuzzy Dataset')
  disp(U)