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Come Converge! Lets Talk About Clustering Alanis Chew and Madeline - PowerPoint PPT Presentation

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What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Come Converge! Lets Talk About Clustering Alanis Chew and Madeline Cope Department of Mathematics and Statistics Youngstown State University

  1. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Come Converge! Let’s Talk About Clustering Alanis Chew and Madeline Cope Department of Mathematics and Statistics Youngstown State University 26th January 2019 Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  2. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Outline What is Clustering? Clustering Techniques Mutual Nearest Neighbor Spectral Clustering Results Acknowledgments Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  3. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments What is clustering? • Data analysis, predicting behaviors Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  4. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments What is clustering? • Data analysis, predicting behaviors • A form of unsupervised classification Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  5. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments What is clustering? • Data analysis, predicting behaviors • A form of unsupervised classification • Group data into meaningful clusters Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  6. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments What is clustering? Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  7. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood • The Mutual Nearest Neighbor clustering algorithm is a hierarchical and agglomerative approach Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  8. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood • The Mutual Nearest Neighbor clustering algorithm is a hierarchical and agglomerative approach Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  9. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood • The Mutual Nearest Neighbor clustering algorithm is a hierarchical and agglomerative approach Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  10. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood • The Mutual Nearest Neighbor clustering algorithm is a hierarchical and agglomerative approach Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  11. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood • The Mutual Nearest Neighbor clustering algorithm is a hierarchical and agglomerative approach Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  12. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments MNN Flowchart Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  13. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments MNN Flowchart Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  14. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood 1. Create a distance matrix, D Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  15. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments MNN Flowchart Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  16. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood 2. Create the nearest neighbor matrix, M 1 Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  17. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments MNN Flowchart Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  18. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood 3. Create the mutual neighborhood value matrix, M 2 Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  19. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments MNN Flowchart Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  20. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood 4. Merge all points with a MNV of 2 Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  21. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Mutual Nearest Neighborhood 4. Merge all points with a MNV of 2 5. Continue merging clusters until the desired number of clusters is reached Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  22. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments MNN Flowchart Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  23. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering • Partitional and graph theoretic Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  24. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering • Partitional and graph theoretic • Represent data as a graph where data points are vertices and edge weights are the similarities between them Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  25. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering • Partitional and graph theoretic • Represent data as a graph where data points are vertices and edge weights are the similarities between them • Uses eigenvalues to perform dimensionality reduction Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  26. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering: Graphs A graph contains a vertex set, an edge set, and a relation that associates each edge with two vertices Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  27. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering: Graphs A graph contains a vertex set, an edge set, and a relation that associates each edge with two vertices Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  28. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering: Graphs A graph contains a vertex set, an edge set, and a relation that associates each edge with two vertices • The vertex set is { A,B,C,D,E } Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  29. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering: Graphs A graph contains a vertex set, an edge set, and a relation that associates each edge with two vertices • The vertex set is { A,B,C,D,E } • The edge set is {{ A,C } , { A,B } , { A,E } , { B,D } , { B,E }} Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  30. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering: Graphs A graph contains a vertex set, an edge set, and a relation that associates each edge with two vertices • The vertex set is { A,B,C,D,E } • The edge set is {{ A,C } , { A,B } , { A,E } , { B,D } , { B,E }} • For example, A and C share a relation Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  31. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  32. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Spectral Clustering Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  33. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Adjacency matrix A : � if connected w ij A ij = 0 otherwise Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

  34. What is Clustering? Clustering Techniques Youngstown State University Results Acknowledgments Adjacency matrix A : � if connected w ij A ij = 0 otherwise A B C D E A 0 1 6 0 0 B 1 0 4 3 1 A = C 6 4 0 1 0 D 0 3 1 0 1 E 0 1 0 1 0 Chew, Cope 26th January 2019 Come Converge! Let’s Talk About Clustering

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