Hi, I have some homework I need to do, I've been at it all the weekend, but now I have to go to sleep and it's not working ok... I don't know if it's a good place to ask, but any hint would be really appreciated.
Here goes my code:

function [ matrix ] = k_means( matrix, clusters, init_option )
%K-MEANS Arranges the data from a matrix into several clusters.
%   matrix: a parsed, standardized matrix 
%   clusters: the number of groups of data to be formed within the matrix
    % Get the number of instances of the data matrix
    [elements,~] = size(matrix);
    % Get an initial random seed for each cluster of data
    seeder = get_Seeds ( matrix, clusters, init_option );
    % Affix a new attribute 'cluster tag' to each element of the data 
    % matrix. It marks membership to a certain cluster. Initial value = 0
    cluster_tag = zeros(elements, 1);
    matrix = [cluster_tag, matrix];
    % Initialize prior distances out of main loop, to control convergence
    prior_distances = zeros(elements, clusters);
    distances = zeros(elements, clusters);
    % Compute membership of each instance of the matrix to a cluster.
    while 1
        % Initialize a container for recalculated centroids
        new_centroids =[];
        % Initialize a matrix to store the distance from each element 
        % to each seed.
        prior_distances = distances
        distances = zeros(elements, clusters);
        % Iterate through each instance of the matrix
        for i = 1:elements
            % Iterate through each seed vector
            for j = 1:clusters
                seed = seeder(j,:);
                % Calculate Euclidean distance between a certain instance 
                % of the matrix and each seed, and store distances to 
                % determine the minimum one.
                one_element = matrix(i,:);
                dist = one_element(2:end) - seed;
                dist = dist.^2;
                dist = sqrt(sum(dist));
                distances(i,j) = dist;
            % CLUSTERIZATION (by tagging)
            % Get the index of the minimum distance to identify the seed
            % that has been used to calculate this distance, and thus assign
            % it as the cluster to the element - matrix(i,:)
            [~, index_cluster] = min(distances(i,:));
            matrix(i,1) = index_cluster;
        % Recalculate centroids according to the current clustering.
        cluster_container = [];
        % Check the elements tagged as belonging to a certain cluster
        % Actually group them into a matrix and calculate their new centroid 
        for j = 1:clusters
            for i = 1:elements
                if matrix(i,1) == j   
                    one_element = matrix(i,:);
                    cluster_container = [cluster_container; one_element(2:end)];                  
            new_centroids = [new_centroids; mean(cluster_container)];
        % Look for stop condition
        if distances == prior_distances 
           % Return sorted matrix with each element tagged with its cluster
           matrix = sort(matrix);
           % Return matrix without the cluster tag, just sorted by cluster
           % (Comment next line to keep the tag column)
           %matrix = matrix(1:end,2:end);
           % Exit the algorithm
            %Repeat the clustering proccess with new centroids
            seeder = new_centroids;