There have the errors after we type FCM(2,'[1 2;2 3;4 5;2 4;3 3;1 5]','[0.1 0.2 0.3 0.4 0.5 0.7;0.9 0.8 0.7 0.6 0.5 0.3]',2, 0.001,5)

function [clustercenter Mugrade] = FCM(c,A,Muinitial,m,tolerancence, imaxiteration )
% Fuzzy C Mean Algorithm
%   A is the data array with nx2, i.e., those are n observations in 2 dimension
%   which we first consider 
%   c is the the number of partitions in the data space.
%  Muinitial is the initial grade of  memberships with cxn
%  m (>=1) is the index of fuzziness
% tolerancence
% imaxiteration
clear all
[n,mm] = size(A);
[c n] = size(Muinitial);
Mu = Muinitial;
tol=tolerancence;
imax=imaxiteration;
%If c ~=cm or n ~= nd 
 %  disp('Error: please check the consistence of the dimension of Muinitial')
 %  else
    for k=1:imax
           M=Mu.^m;
           V=sum(M');
           DIAGONAL=diag(V);
           CLUSTERCENTER = DIAGONAL*M*A;
           B=-1*A';
           Munew=(CLUSTERCENTER*B).^(-2/(m-1));
           W=sum(Munew');
           DIAGONAL2=diag(W)
           Munorrmal=DIAGONAL2*Munew;
           tori=sum(sum(Mu-Munormal))
           if toli < tol
               break 
           end
           if k == imax
               fprint('Solution was not obtained in %k iteration', imax)
               break
           end
           Mu = Munormal;k=k+1;
    end
clustercenter =  CLUSTERCENTER
Mugrade = Munormal 
for k=1:n
    plot(A(k,1),A(k,2))
    hold on
end
for k=1:c
    plot(clustercenter(k,1),clustercenter(k,2))
    hold on
end
hold off
%end

Answer 1

[Walter Roberson]

You pass A in as a parameter. You execute "clear all" at the start of your routine, thus clearing all variables. You then try to reference A in the very next line.

Any time you have an urge to use "clear all", you should go and lie down until the urge goes away.

[Walter Roberson]

You are going to have problems in getting the desired output if you try to use A and MuInitial as character strings, the way you show in your Title.

Answer 2

[Junaid]

Kindly reformat your code. Make it readable.

Answer 3

[Ming-Gar Lee]

 [clustercenter Mugrade] = FCMnew(c,A,Muinitial,m,tolerancence, imaxiteration )
% Fuzzy C Mean Algorithm
%   Input variables
%   A is the data array with nx2, i.e., those are n observations in 2 dimension
%   which we first consider 
%   c is the the number of partitions in the data space.
%  Muinitial is the initial grade of  memberships with cxn
%  m (>=1) is the index of fuzziness
% tolerancence
% imaxiteration
% Output variables
% clustercenter is the center of cluster about the given data 
% array  A
% Mugrade is the fuzzy grade about the the given data array A 
[n,mm] = size(A);
[c, n] = size(Muinitial);
Mu = Muinitial;
tol=tolerancence;
imax=imaxiteration;
%If c ~=cm or n ~= nd 
 %  disp('Error: please check the consistence of the dimension of Muinitial')
 %  else
    for k=1:imax
           M=Mu.^m;
           M1=M';
           V=sum(M1);
           DIAGONAL1=diag(V);
           CLUSTERCENTER = (DIAGONAL1\M)*A;
           B=-1*A';
           Munew=(CLUSTERCENTER*B).^(-2/(m-1));
           W=sum(Munew');
           DIAGONAL2=diag(W);
           Munormal=DIAGONAL2\Munew;
           tori=sum(sum(Mu-Munormal));
           if tori < tol
               break 
           end
           if k == imax
               fprint('Solution was not obtained in %k iteration', imax)
               break
           end
           Mu = Munormal;k=k+1;
    end
clustercenter =  CLUSTERCENTER
Mugrade = Munormal 
x=A(:,1);
y=A(:,2);
plot(x,y,'or','MarkerSize',12)
hold on
xc=clustercenter(:,1);
yc=clustercenter(:,2);
plot(xc,yc,'+b','MarkerSize',15)
hold off