My own implementation of kMeans Machine Learning algorithm in Matlab

kmyself.m

function[r,l] = kmyself(dataSet, k)

    % Determine the size of dataSet
    [nRow, nCol] = size(dataSet);
    
    % Empty array for cluster assesment
    clusterAssment = zeros(nRow,2);
    
    % Setup centroid and choose on to start of with
    centroids = zeros(k,nCol);
    for j = 1:1:nCol
        minJ = min(dataSet(:,j));
        rangeJ = max(dataSet(:,j) - minJ);
        centroids(:,j) = minJ + rangeJ * rand(k,1);
    end
    
    % Conditional looping variable
    clusterChanged = true;
    
    while (clusterChanged)
        clusterChanged = false;
        
        for i = 1:1:nRow
            minDist = Inf;
            minIndex = -1;
            for j = 1:1:k
                distJI = dist(centroids(j,:), dataSet(i, :));
                if distJI < minDist
                    minDist = distJI;
                    minIndex = j;
                end
            end
            
            if clusterAssment(i,1) ~= minIndex
                clusterChanged = true;
            end
            
            clusterAssment(i,:) = [minIndex,minDist^2];
        end
        
        
  
        for cent = 1:1:k
            
            ptsInClust = [];
            
            for item = 1:1:nRow
                if (clusterAssment(item,1) == cent)
                    ptsInClust = [ptsInClust; dataSet(item, :)];
                end
            end
            
           centroids(cent,:) = mean(ptsInClust);
        end
         
    end

    r = clusterAssment;
    l = centroids;
end 

function d = dist(A,B)
    % d = ((A(1)-B(1))^2+(A(2)-B(2))^2)^0.5    
    d = sqrt(sum(power(A-B, 2)));
end