How to draw as scatter of PCA with different color of clusters after K-means algorithm using matlab?

Question

I am trying to build implementation code for the k-mean algorithm using matlab. I am learning and new to using Matlab. Somehow I built implementation code for the k-mean algorithm by searching matlab functions on googling youtube. I set the initial 3 initial centroids and had aperture diaphragms, and these three centroids go in the right direction to make 3 clusters when I checked it. However, I really do not understand and cannot find the source from the Internet that I want. Can someone help me how to draw a PCA 2D scatterplot with different colors of three clusters?

This is my code implementation for k-mean,

clear; clc; close all;
load iris.xls
DataSet = iris;
Dim = size(DataSet);

load Iris_Initial_Centroids.xls
Centroid = Iris_Initial_Centroids;
Dim_Cen = size(Centroid);

Centroid1 = Centroid(1,:);
Centroid2 = Centroid(2,:);
Centroid3 = Centroid(3,:);

n = input('Enter the number of Iteration : ');

for i=1:1:n
    count1 = 0;
    Mean1 = zeros(1,4);
    count2 = 0;
    Mean2 = zeros(1,4);
    count3 = 0;
    Mean3 = zeros(1,4);

    for j=1:1:Dim(1,1)
        Pattern1(j)=sqrt((Centroid1(1,1)-DataSet(j,1))^2+(Centroid1(1,2)-DataSet(j,2))^2+(Centroid1(1,3)-DataSet(j,3))^2+(Centroid1(1,4)-DataSet(j,4))^2);
        Pattern2(j)=sqrt((Centroid2(1,1)-DataSet(j,1))^2+(Centroid2(1,2)-DataSet(j,2))^2+(Centroid2(1,3)-DataSet(j,3))^2+(Centroid1(1,4)-DataSet(j,4))^2);
        Pattern3(j)=sqrt((Centroid3(1,1)-DataSet(j,1))^2+(Centroid3(1,2)-DataSet(j,2))^2+(Centroid3(1,3)-DataSet(j,3))^2+(Centroid1(1,4)-DataSet(j,4))^2);
        closestDistance = [Pattern1(j) Pattern2(j) Pattern3(j)];
        minimum = min(closestDistance);
    if (minimum == Pattern1(j))
        count1 = count1+1;
        Mean1 = Mean1 + DataSet(j,:);
    else if (minimum == Pattern2(j))
            count2 = count2 + 1;
            Mean2 = Mean2 + DataSet(j,:);
        else
            count3 = count3+1;
            Mean3 = Mean3 + DataSet(j,:);
        end
    end
    end

    Centroid1 = Mean1/count1;
    Centroid2 = Mean2/count2;
    Centroid3 = Mean3/count3;
    %plot(i, Centroid1, '.');
    %plot(i, Centroid2, '.');
    %plot(i, Centroid3, '.');
end

**[coeff.score.latent] = pca(DataSet);
newDataSet = score(:,1:2);
plot(newDataSet(:,1),newDataSet(:,2),'.');**

, PCA. 2D PCA , , rgb. ? - ? Matlab .

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Answer 1

:

[coeff.score.latent] = pca(DataSet);

. .

[coeff,score,latent] = pca(DataSet);