k-means clustering algorithm

I have a data with dimension 1801 by 88, to be clustered into k regions. Intially, all the centres are assigned randomly. the 'i' th data , sig is assigned to the nearest data cluster, c based on minimum Euclidean distance, where i=1:1801 and j=1:k. After completeing the assigning process for all the data, the new position of centre is measured according to

I have written the accordingly, but is not run and I am unable to understand about new position of the centre, although I have written. But not clear to me. How to add number of iteration in this code.

please help me

load signal.dat;
sig=signal(:,1:88);
[maxrow,maxcol]=size(sig);
k=1;
for i=1:k
p(i,:)=randperm(size(sig,1));
end
for i=1:k
    for j=1:88
    c(i,j)=sig(p(i),j);%random initialization
    end
end
temp=zeros(maxrow,1);
while 1
    for i1=1:1801
        for j=1:88
             d(:,j)=sum(dist2(sig(:,j),c(1,j)));
        end
        [z,g]=max(d,[],1);
        if g==temp
            break;
        else
            temp=g; 
        end
        for i=1:k
            f=find(g==i);
            if f
                c(i,:)=mean(sig(find(g==i),:),1);
            end
        end
    end
end