2. Unsupervised Learning and Clustering
Padhraic Smyth
Information and Computer Science
ICS 175, Spring 2002

3. Example: Data in 2 Clusters
Feature 2
Feature 1

4. “Compact” Clustering: Low TSE
Feature 2
Cluster Center 2
Cluster Center 1
Feature 1

5. “Non-Compact” Clustering: High TSE
Feature 2
Cluster Center 2
Cluster Center 1
Feature 1

6. Original Data (2 dimensions)

7. Initial Cluster Centers for K-means (K=2)

8. Update Memberships (Iteration 1)

9. Update Cluster Centers at Iteration 2

10. Update Memberships (Iteration 2)

11. Update Cluster Centers at Iteration 3

12. Update Memberships (Iteration 3)

13. Update Cluster Centers at Iteration 4

14. Updated Memberships (Iteration 4)

15. Clustering Images We can also cluster sets of images into groups
now each vector = a full image (dimensions 1 x (mxn))
N images of size m x n
convert to a matrix with N rows and (m x n) columns
just use image_to_matrix.m
call kmeans with D = this matrix
kmeans is now clustering in an (m x n) dimensional space
kmeans will group the images into K groups

16. Example: First 5 Individuals, K = 2
Cluster 1
Cluster 2

17. Example: 2nd 5 individuals, K = 2
Cluster 1
Cluster 2

18. All Individuals, Happy Faces, K=5