Classifiying the data points

data = rand(100, 4)
x = data(:, 1);
y = data(:, 2)
z = data(:, 3);
d = data(:, 4);
numClasses = 10
% Find classes based on 4th column.
indexes = kmeans(d, numClasses);
% Make up a colormap with 10 colors
cmap = jet(10);
% Assign a color to each data point based on it's class
markerColors = zeros(10, 3); % 10 colors.
for k = 1 : size(data, 1)
	thisClassNumber = indexes(k)
	markerColors(k, :) = cmap(thisClassNumber, :);
end
scatter3(x, y, z, 50, markerColors, 'filled');
fontSize = 20;
xlabel('X', 'FontSize', fontSize);
ylabel('Y', 'FontSize', fontSize);
zlabel('Z', 'FontSize', fontSize);

There is a marker at each (x,y,z) location and its color depends on which of the 10 classes that point's "d" value got classified into.

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Mehmet Volkan Ozdogan 2019-7-3

在 MATLAB Online 中打开

clc;clear;
x=xlsread('ping1.xlsx', 'A:A');
y=xlsread('ping1.xlsx', 'B:B');
z=xlsread('ping1.xlsx', 'C:C');
a=xlsread('ping2.xlsx', 'A:A');
b=xlsread('ping2.xlsx', 'B:B');
c=xlsread('ping2.xlsx', 'C:C');
xyz=[x y z];
abc=[a b c];
rng(1);
[idx1,C1] = kmeans(xyz,100,'distance','sqEuclidean','MaxIter',500, 'Replicates', 10);
[idx2,C2] = kmeans(abc,100,'distance','sqEuclidean','MaxIter',500, 'Replicates', 10);
for i=1:100
t(i)=i
end
t=t';
for i=1:100
u(i)=i;
end
u=u';
[dist,idx3] = pdist2(xyz, C1, 'euclidean', 'Smallest',1);
newVar = xyz(idx3 ,:);
plot3(newVar(:,1), newVar(:,2), newVar(:,3), 'bx');
text(newVar(:,1)+0.02,newVar(:,2),newVar(:,3),num2str(t),'FontSize',7);
hold on;
xlabel ('x - axis', 'fontsize', 12);
ylabel ('y - axis', 'fontsize', 12);
zlabel ('z - axis', 'fontsize', 12);
grid
[dist2,idx4] = pdist2(abc, C2, 'euclidean', 'Smallest',1);
newVar2 = abc(idx4 ,:);
%plot3(newVar2(:,1), newVar2(:,2), newVar2(:,3), 'r*');
%text(newVar2(:,1),newVar2(:,2),newVar2(:,3),num2str(u),'FontSize',7);
axis equal;
newVar3 = mean (newVar);
newVar4 = mean (newVar2),
newVar5 = (newVar3 + newVar4)/ 2;
%plot3(newVar5(:,1), newVar5(:,2), newVar5(:,3), 'go');
m=[newVar(:,1) newVar(:,2) newVar(:,3)];
n=[newVar2(:,1) newVar2(:,2) newVar2(:,3)];
o=[newVar5(:,1) newVar5(:,2) newVar5(:,3)];
d1 = pdist2(newVar5,newVar);                                                % distance(s) (many) between O and X
[~,idx5] = pdist2(newVar2,newVar,'euclidean','smallest',1);                 % indices of nearest * for EACH X
d2 = pdist2(newVar5,newVar2(idx5,:));                                       % distance(s) between O and * (nearest to X)
D=d2 - d1
x1 = m(:, 1 ); 
y1 = m(:, 2 ); 
z1 = m(:, 3 );  
numClasses = 10 
% Find classes based on 4th column . 
indexes = kmeans(D(:), numClasses ); 
% Make up a colormap with 10 colors 
cmap = jet(10 ); 
% Assign a color to each data point based on it's class 
markerColors = zeros(10, 3); % 10 colors . 
for k = 1 : size(m, 1 ) 
thisClassNumber = indexes(k ) 
markerColors(k, :) = cmap(thisClassNumber , :); 
end 
scatter3(x1, y1, z1, 50, markerColors, 'filled '); 
fontSize = 20 ; 
xlabel('X', 'FontSize', fontSize ); 
ylabel('Y', 'FontSize', fontSize ); 
zlabel('Z', 'FontSize', fontSize);