Something like this?
% Generate sample data with two clusters
data = [randn(100,1); 5+randn(100,1)]; % 100 points around 0 and 100 points around 5
% Perform Kernel Density Estimation
[f, xi] = ksdensity(data);
% Plot the KDE
figure;
plot(xi, f);
xlabel('Data Values');
ylabel('Density');
title('Kernel Density Estimation');
% Find Clusters by Identifying Peaks
[pks, locs] = findpeaks(f, xi);
hold on;
plot(locs, pks, 'or'); % Plot peaks
legend('KDE', 'Peaks');
hold off;
% Function to find the nearest peak for a given point
function idx = findNearestPeak(point, peaks)
[~, idx] = min(abs(peaks - point));
end
% Assign points to the nearest cluster peak
clusterAssignments = arrayfun(@(x) findNearestPeak(x, locs), data);
% Visualize the Clustering Result
figure;
for i = 1:length(locs) % For each cluster
clusterData = data(clusterAssignments == i);
plot(clusterData, zeros(size(clusterData)), 'o', 'DisplayName', sprintf('Cluster %d', i));
hold on;
end
plot(locs, zeros(size(locs)), 'kx', 'MarkerSize', 10, 'DisplayName', 'Cluster Centers');
hold off;
xlabel('Data Values');
title('Data Clustering Result');
legend;
