ml2/lw2

Labwork 2 - Clustering

K-means

Select two datasets from UCI, and for each of them:

1. Run experiments with k-means. Explain the experimental protocol.
2. Explain the centroid initialization of the k-means used in the first question.
3. Analyze and compare the results with different values of k.
4. Calculate the clustering quality (any criteria in slides).

Subspace clustering

1. Select one high-dimensional dataset (>100 features)
2. Use PCA or SVD to visualize the data distribution in 2D/3D.
3. Apply any clustering method (DBSCAN, K-means, SOM, GNG, etc.) for the selected datasets in 2D/3D after using PCA/SVD. Compare the performance before and after the dimensionality reduction.
4. Select a random subspace of the dataset. Re-apply any clustering method on the subspace. Discuss and compare the performance with the previous results.