assess the performance of k-means adapted to supervised classification with k=10, 20, 30 in matlab

main.m
% Load your dataset and split it into training and testing sets
[data, labels] = load_dataset('data.csv');
[train_data, train_labels, test_data, test_labels] = split_dataset(data, labels, 0.8);

% Define a range of values for k
ks = [10, 20, 30];

% Train and evaluate k-means models for each value of k
accuracies = zeros(size(ks));
for i = 1:length(ks)
    % Train k-means model on the training set
    k = ks(i);
    [idx, centroids] = kmeans(train_data, k);

    % Predict the clusters for instances in the testing set
    predicted_clusters = kmeans(test_data, k, 'Start', centroids);

    % Assign instances to the majority class in their cluster
    predicted_labels = assign_labels(predicted_clusters, train_labels);

    % Compute the accuracy of the classifier
    accuracies(i) = sum(predicted_labels == test_labels) / length(test_labels);
end

% Plot the accuracy vs k graph
plot(ks, accuracies);
xlabel('k');
ylabel('Accuracy');
942 chars
29 lines