how to k cluster in matlab

To perform k-means clustering in MATLAB, you can use the built-in kmeans function. Here is an example usage:

main.m
% Load sample data
load fisheriris

% Define number of clusters
k = 3;

% Perform clustering
[idx, C] = kmeans(meas, k);

% Plot results
figure;
gscatter(meas(:,1), meas(:,2), idx);
hold on;
plot(C(:,1), C(:,2), 'kx', 'MarkerSize', 15, 'LineWidth', 3);
legend({'Cluster 1', 'Cluster 2', 'Cluster 3', 'Centroids'}, 'Location', 'NW');
xlabel('Sepal length');
ylabel('Sepal width');
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In this example, we load the fisheriris dataset and specify that we want to cluster the data into 3 groups (k=3). We then run the kmeans function on the meas variable, which contains the measurements of sepal length and width for each iris. The resulting idx variable contains the cluster index for each data point, and C contains the centroid coordinates for each cluster.

We then plot the results using the gscatter function to plot the data points colored by their cluster index, and the plot function to plot the cluster centroids as black crosses. The resulting plot shows how the data has been partitioned into the three clusters.