how to compare or analyse the results of different unsupervised algorithms in matlab?

I am using unsupervised (clustering) methods on a protein sequence , i want to analyse and compare their results through plotting, how could i do this in matlab?

1 commentaire

Hi,
From my understanding, you are looking for functions to help in comparing the performance of unsupervised machine learning algorithms via a plotting approach.
You can use scatter plots, silhouette plots, or dendrograms to visualize and compare the clustering results. The function gscatter” can be used to scatter plot by group - https://www.mathworks.com/help/stats/gscatter.html.
Silhouette plots can be created using the silhouette” function from clustered data using different distance metrics - https://www.mathworks.com/help/stats/silhouette.html?s_tid=doc_ta.
Dendrograms can be created using the function “dendrogram
. This function generates a dendrogram plot of the hierarchical binary cluster tree - https://www.mathworks.com/help/stats/dendrogram.html?s_tid=doc_ta.
Here is some starter code for these functions –
data = randn(100, 10); % Example data (100 sequences, 10 features)
% Apply K-means clustering
numClusters = 3;
[idxKmeans, C] = kmeans(data, numClusters);
% Apply Hierarchical clustering
Z = linkage(data, 'ward');
idxHierarchical = cluster(Z, 'maxclust', numClusters);
% Plot K-means results
figure;
subplot(2, 2, 1);
gscatter(data(:,1), data(:,2), idxKmeans);
title('K-means Clustering');
xlabel('Feature 1');
ylabel('Feature 2');
% Plot Silhouette for K-means
subplot(2, 2, 2);
silhouette(data, idxKmeans);
title('Silhouette Plot for K-means');
% Plot Hierarchical clustering results
subplot(2, 2, 3);
dendrogram(Z);
title('Hierarchical Clustering Dendrogram');
% Plot Silhouette for Hierarchical clustering
subplot(2, 2, 4);
silhouette(data, idxHierarchical);
title('Silhouette Plot for Hierarchical Clustering');
% Additional Visualization: Compare clusters in a 3D plot
figure;
scatter3(data(:,1), data(:,2), data(:,3), 50, idxKmeans, 'filled');
title('3D Scatter Plot of Clusters');
xlabel('Feature 1');
ylabel('Feature 2');
zlabel('Feature 3');
grid on;
Hope this helps!

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Question posée :

le 28 Fév 2014

Commenté :

le 24 Oct 2024

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