How to use kmeans clustering for face images present in database using eigenfaces features in matlab?

My code uses eigenfaces features to recognize faces.Iam having 20 images in my database.I have to make 3 clusters.Iam using kmeans clustering method.This code can calculate maximum and minimum eucledian distance for each input image taken from database using weight vector.Using this feature i have to make cluster.In matlab software kmeans command is available IDX=kmeans(data,k).I took minimum euclidean distance as 'data' and k=3.It is making minimum euclidean distance of input image as 3 clusters.But i have to cluster 20 face images from database as 3 clusters.How it can be done.What input parameter has to be given for kmeans command.Guide me.

          % Face recognition 
        clear all
        close all
        clc

    % number of images on your training set.
    M=20;

    %Chosen std and mean. 
    %It can be any number that it is close to the std and mean of most of the images.
    um=100;
    ustd=80;

    %read and show images(bmp);
    S=[];   %img matrix
    figure(1);
    for i=1:M
        str=strcat(int2str(i),'.jpg');   %concatenates two strings that form the name of the image
        eval('img=imread(str);');
        subplot(ceil(sqrt(M)),ceil(sqrt(M)),i)
        imshow(img)
        if i==3
            title('Training set','fontsize',18)
        end
        drawnow;
           [irow icol d]=size(img); % get the number of rows (N1) and columns (N2)
           temp=reshape(permute(img,[2,1,3]),[irow*icol,d]);     %creates a (N1*N2)x1 matrix
        S=[S temp];         %X is a N1*N2xM matrix after finishing the sequence
                            %this is our S
    end

    %Here we change the mean and std of all images. We normalize all images.
    %This is done to reduce the error due to lighting conditions.
    for i=1:size(S,2)
        temp=double(S(:,i));
        m=mean(temp);
        st=std(temp);
        S(:,i)=(temp-m)*ustd/st+um;
    end

    %show normalized images
    figure(2);
    for i=1:M
        str=strcat(int2str(i),'.jpg');
        img=reshape(S(:,i),icol,irow);
        img=img';
       % eval('imwrite(img,str)');   
        subplot(ceil(sqrt(M)),ceil(sqrt(M)),i)
        imshow(img)
        drawnow;
        if i==3
            title('Normalized Training Set','fontsize',18)
        end
    end

    %mean image;
    m=mean(S,2);   %obtains the mean of each row instead of each column
    tmimg=uint8(m);   %converts to unsigned 8-bit integer. Values range from 0 to 255
    img=reshape(tmimg,icol,irow);    %takes the N1*N2x1 vector and creates a N2xN1 matrix
    img=img';       %creates a N1xN2 matrix by transposing the image.
    figure(3);
    imshow(img);
    title('Mean Image','fontsize',18)

    % Change image for manipulation
    dbx=[];   % A matrix
    for i=1:M
        temp=double(S(:,i));
        dbx=[dbx temp];
    end

    %Covariance matrix C=A'A, L=AA'
    A=dbx';
    L=A*A';
    % vv are the eigenvector for L
    % dd are the eigenvalue for both L=dbx'*dbx and C=dbx*dbx';
    [vv dd]=eig(L);
    % Sort and eliminate those whose eigenvalue is zero
    v=[];
    d=[];
    for i=1:size(vv,2)
        if(dd(i,i)>1e-4)
            v=[v vv(:,i)];
            d=[d dd(i,i)];
        end
     end

     %sort,  will return an ascending sequence
     [B index]=sort(d);
     ind=zeros(size(index));
     dtemp=zeros(size(index));
     vtemp=zeros(size(v));
     len=length(index);
     for i=1:len
        dtemp(i)=B(len+1-i);
        ind(i)=len+1-index(i);
        vtemp(:,ind(i))=v(:,i);
     end
     d=dtemp;
     v=vtemp;

    %Normalization of eigenvectors
     for i=1:size(v,2)       %access each column
       kk=v(:,i);
       temp=sqrt(sum(kk.^2));
       v(:,i)=v(:,i)./temp;
    end

    %Eigenvectors of C matrix
    u=[];
    for i=1:size(v,2)
        temp=sqrt(d(i));
        u=[u (dbx*v(:,i))./temp];
    end

    %Normalization of eigenvectors
    for i=1:size(u,2)
       kk=u(:,i);
       temp=sqrt(sum(kk.^2));
        u(:,i)=u(:,i)./temp;
    end

    % show eigenfaces;
    figure(4);
    for i=1:size(u,2)
        img=reshape(u(:,i),icol,irow);
        img=img';
        img=histeq(img,255);
        subplot(ceil(sqrt(M)),ceil(sqrt(M)),i)
        imshow(img)
        drawnow;
        if i==3
            title('Eigenfaces','fontsize',18)
        end
    end

    % Find the weight of each face in the training set.
    omega = [];
    for h=1:size(dbx,2)
        WW=[];    
        for i=1:size(u,2)
            t = u(:,i)';    
            WeightOfImage = dot(t,dbx(:,h)');
            WW = [WW; WeightOfImage];
        end
        omega = [omega WW];
    end

    % Acquire new image
    % Note: the input image must have a bmp or jpg extension. 
    %       It should have the same size as the ones in your training set. 
    %       It should be placed on your desktop
    ed_min=[];
    for k=0:3
    InputImage = input('Please enter the name of the image and its extension \n','s');
    InputImage = imread(strcat('F:\input image\',InputImage));
    figure(5)
    subplot(1,2,1)
    imshow(InputImage); colormap('gray');title('Input image','fontsize',18)
    %InImage=reshape(double(InputImage),irow*icol,1); 
    InImage=reshape(permute((double(InputImage)),[2,1,3]),[irow*icol,1]);
    temp=InImage;
    me=mean(temp);
    st=std(temp);
    temp=(temp-me)*ustd/st+um;
    NormImage = temp;
    Difference = temp-m;

    p = [];
    aa=size(u,2);
    for i = 1:aa
        pare = dot(NormImage,u(:,i));
        p = [p; pare];
    end
    %ReshapedImage = m + u(:,1:aa)*p;    %m is the mean image, u is the eigenvector
    %ReshapedImage = reshape(ReshapedImage,icol,irow);
    %ReshapedImage = ReshapedImage';
    %show the reconstructed image.
    %subplot(1,2,2)
    %imagesc(ReshapedImage); colormap('gray');
    %title('Reconstructed image','fontsize',18)

    InImWeight = [];
    for i=1:size(u,2)
        t = u(:,i)';
        WeightOfInputImage = dot(t,Difference');
        InImWeight = [InImWeight; WeightOfInputImage];
    end

    %ll = 1:M;
    %figure(68)
    %subplot(1,2,1)
    %stem(ll,InImWeight)
    %title('Weight of Input Face','fontsize',14)

    % Find Euclidean distance
    e=[];
    for i=1:size(omega,2)
        q = omega(:,i);
        DiffWeight = InImWeight-q;
        mag = norm(DiffWeight);
        e = [e mag];

    end

    %kk = 1:size(e,2);
    %subplot(1,2,2)
    %stem(kk,e)
    %title('Eucledian distance of input image','fontsize',14)
    format long

    MaximumValue=max(e)
    MinimumValue=min(e)

    ed_min=[ed_min MinimumValue];

    theta=3.593489431882765e+04;
    %disp(e)

    if(MinimumValue<=theta)
        fprintf('recognized face\n');
    else
        fprintf('unrecognized face\n');
    end

    end

    disp(ed_min);

    [IDX,C] = kmeans(ed_min,3)