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Unsupervised Learning and Clustering. Padhraic Smyth Information and Computer Science ICS 175, Spring 2002. Example: Data in 2 Clusters. Feature 2. Feature 1. “ Compact ” Clustering: Low TSE. Feature 2. Cluster Center 2. Cluster Center 1. Feature 1.
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Unsupervised Learning and Clustering Padhraic Smyth Information and Computer Science ICS 175, Spring 2002
Example: Data in 2 Clusters Feature 2 Feature 1
“Compact” Clustering: Low TSE Feature 2 Cluster Center 2 Cluster Center 1 Feature 1
“Non-Compact” Clustering: High TSE Feature 2 Cluster Center 2 Cluster Center 1 Feature 1
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
Example: First 5 Individuals, K = 2 Cluster 1 Cluster 2
Example: 2nd 5 individuals, K = 2 Cluster 1 Cluster 2