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CC282 Unsupervised Learning (Clustering) Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Lecture 07 – Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Clustering (introduction) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Clustering (introduction) ,[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Clustering (introduction – ctd) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Clustering – an early application example ,[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008 ,[object Object],Diagram from Google Images
Clustering – Major approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Exclusive (partitioning) clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
K -means clustering algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
K-means – an example ,[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008 *note for one dimensional data, Manhattan distance=Euclidean distance
K-means – an example (ctd) ,[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
K-means – Example (ctd) ,[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
K-Means – Example (ctd) ,[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
K - means ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
K-means in MATLAB ,[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Agglomerative clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Dendrogram (ctd) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008 A dendrogram example
Hierarchical clustering - algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Hierarchical clustering algorithm– step 3 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Hierarchical clustering algorithm– step 3 Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Hierarchical clustering – an example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Hierarchical clustering – an example (ctd) ,[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Hierarchical clustering – an example (ctd) –using MATLAB ,[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Comparing agglomerative vs exclusive clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Overlapping clustering –Fuzzy C-means algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Overlapping clusters? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Fuzzy C -means algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Fuzzy C -means algorithm – an example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Fuzzy C -means algorithm –using MATLAB ,[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Fuzzy C -means algorithm –using MATLAB ,[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Clustering validity problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Clustering validity problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Clustering Quality Criteria ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Davies-Bouldin index ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Davies-Bouldin index example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008 Note, variance of one element is zero and centroid is simply the element itself
Davies-Bouldin index example (ctd) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Davies-Bouldin index example (ctd) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008
Lecture 7: Study Guide ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture 7 slides for CC282 Machine Learning, R. Palaniappan, 2008

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CC282 Unsupervised Learning (Clustering) Lecture 7 slides for ...

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