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Unsupervised Learning and Clustering

Learn about unsupervised learning techniques and clustering algorithms in information science through this comprehensive guide. Explore topics such as compact and non-compact clustering, k-means algorithm iterations, and clustering images effectively. Gain insights into grouping data into clusters for improved analysis and visualization.

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Unsupervised Learning and Clustering

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

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

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

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

  5. Original Data (2 dimensions)

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

  7. Update Memberships (Iteration 1)

  8. Update Cluster Centers at Iteration 2

  9. Update Memberships (Iteration 2)

  10. Update Cluster Centers at Iteration 3

  11. Update Memberships (Iteration 3)

  12. Update Cluster Centers at Iteration 4

  13. Updated Memberships (Iteration 4)

  14. 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

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

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

  17. All Individuals, Happy Faces, K=5

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