For plotting each cluster with a unique color, you should plot them individually using scatter function in a loop. Refer the example below where I have used random data to fit a GMM and then plot the clusters with unique colors:
% Generate example data: 5000 points, 3 true clusters
rng(1); % For reproducibility
X = [mvnrnd([2 2], eye(2), 1667);
mvnrnd([7 7], eye(2), 1666);
mvnrnd([2 7], eye(2), 1667)];
% Fit a Gaussian Mixture Model with 3 clusters (use 15 for your case)
numClusters = 3;
gmm = fitgmdist(X, numClusters);
% Get posterior probabilities and means
probabilities = posterior(gmm, X); % (5000,3)
means = gmm.mu; % (3,2)
% Assign each point to the cluster with highest probability
[~, clusterIdx] = max(probabilities, [], 2);
The lines function returns the lines colormap as a three-column array with the same number of rows as the colormap for the current figure.You can read more about the function here: https://www.mathworks.com/help/matlab/ref/lines.html
% Define colors
colors = lines(numClusters);
Now, the clusters are plotted iteratively with unique colors:
% Plot clusters
figure
hold on
for k = 1:numClusters
idx = (clusterIdx == k);
scatter(X(idx,1), X(idx,2), 15, colors(k,:), 'filled');
end
% Plot cluster means
plot(means(:,1), means(:,2), 'rd', 'MarkerSize',10, 'LineWidth',2)
hold off
xlabel('X1');
ylabel('X2');
title('EM Clustering Results');
legend('Clusters','Cluster Centers');
