My K-means program has malfunctionality; please help me to run it properly

Hello everyone,

I wrote some scripts for K-means clustering, but sometimes it is not able to assign some points to its correct cluster. As it seems, there are some problems that I'm not to find them and revise them. I put my scripts here and need your idea to rearrange the program for running appropriately. In addition, the data of these codes were attached, and you can find it at the bottom of this post.

clc;
clear;
close all;
load fisheriris
ClustersNumber=2;
X=meas(:,3);
Y=meas(:,4);
Random=randi([1 numel(X)],ClustersNumber,1);

for i=1:ClustersNumber

     Mean.X(i)=X(randi(Random(i)));
     Mean.Y(i)=Y(randi(Random(i)));

end

iteration=0;

while 1

% The distance of each point from the mean of each cluster

    if iteration>100

        for j=1:numel(X)

            for i=1:ClustersNumber

            Cluster.Distance(i,j)=sqrt((X(j)-Mean.X(i))^2+(Y(j)-Mean.Y(i))^2);

              Cluster.Distance(Cluster.Distance==0)=NaN;

            end
             Min(j)=min(Cluster.Distance(:,j));

         end

    %%Assigning the points to each nearest cluster respecting the means

    for i=1:numel(X)

            if Cluster.Distance(1,i)==Min(i)
                Class(1).data(i)=Min(i);

                Class(1).X(i)=X(i);
                Class(1).Y(i)=Y(i);

            else

               Class(2).data(i)=Min(i);

                Class(2).X(i)=X(i);
                Class(2).Y(i)=Y(i);

            end  
    end

    % Updating the means
    for i=1:ClustersNumber

         Mean.X(i)=mean(Class(i).X(Class(i).X~=0));
         Mean.Y(i)=mean(Class(i).Y(Class(i).Y~=0));

    end
    break;

    end

 iteration=iteration+1;

end

scatter(Class(1).X,Class(1).Y,'+'); hold on; scatter(Class(2).X,Class(2).Y,'s');

Thanks

[Image Analyst]

All I get is this:

>> load fisheriris
Error using load
Unable to read file 'fisheriris'. No such file or directory.

How do you think we can solve that so we can help you? Please read this.

[Federico Frics]

Thanks for your response. The fisheriris is a data incorporated into matlab directory by default. However, if you did'nt manage to find the data, I attached the data to the first post for you.

Thanks

Answer 1

[Federico Frics]

Hi there,

As no one was not able to help me, I found where the problem is, and revised it.Therefore, I place my final codes here to help other people.

clc; clear; close all; load fisheriris ClustersNumber=2; X=meas(:,3); Y=meas(:,4); Random=randi([1 numel(X)],ClustersNumber,1);

 %%Generate the initial clusters randomly

 Class(1).X=X(1:(randi([1 80],1)));
 Class(1).Y=Y(1:numel(Class(1).X));
 Class(2).X=X(numel(Class(1).X)+1:end);
 Class(2).Y=Y(numel(Class(1).X)+1:end);

% Determine the mean of each initial cluster for k=1:ClustersNumber

     Mean.X(k)=mean(Class(k).X);
     Mean.Y(k)=mean(Class(k).Y);

     end

%% Main loop iteration=0;

while 1

    if iteration>100

        % The distance of each point from the mean of the clusters 

        for j=1:numel(X)

            for i=1:ClustersNumber

            Cluster.Distance(i,j)=sqrt((X(j)-Mean.X(i))^2+(Y(j)-Mean.Y(i))^2);

              Cluster.Distance(Cluster.Distance==0)=NaN;

            end

            % Compute the minimum distance of each point from the classes

             Min(j)=min(Cluster.Distance(:,j));

         end

    %%Assign the points to each nearest cluster respecting the means

    for i=1:numel(X)

            if Cluster.Distance(1,i)==Min(i)
                NewClass(1).data(i)=Min(i);

                NewClass(1).X(i)=X(i);
                NewClass(1).Y(i)=Y(i);

            elseif Cluster.Distance(2,i)==Min(i)

               NewClass(2).data(i)=Min(i);

                NewClass(2).X(i)=X(i);
                NewClass(2).Y(i)=Y(i);

            end  
    end

    % Update the means
        for k=1:ClustersNumber

            NewClass(k).X( NewClass(k).X==0)=[];
            NewClass(k).Y( NewClass(k).Y==0)=[];
            Mean.X(k)=mean(NewClass(k).X);
            Mean.Y(k)=mean(NewClass(k).Y);

        end

    break;

    end

 iteration=iteration+1;

end

scatter(NewClass(1).X,NewClass(1).Y,'+'); hold on; scatter(NewClass(2).X,NewClass(2).Y,'s'); scatter(Mean.X(1),Mean.Y(1),'*'); scatter(Mean.X(2),Mean.Y(2));

Answer 2

[Federico Frics]

You can find the data in the first post. Thanks