Classification and Clustering
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This document provides an overview of clustering and classification techniques. It defines clustering as organizing objects into groups of similar objects and discusses common clustering algorithms like k-means and hierarchical clustering. It also provides examples of how k-means works and references for further information.
Classification and Clustering
- 1. Clustering and Classification Presented by: Yogendra, Govinda, Lov, Sunena
- 2. Outline • Background • Classification • Clustering • Examples • References
- 3. Background • Clustering is “the process of organizing objects into groups whose members are similar in some way”. • A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.
- 4. Clustering
- 5. Clustering • Organizing objects into group. Cluster analysis Components • Similarity(Distance) Measure • Clustering Algorithm
- 6. Similarity Measure • Distance between two data points
- 7. Clustering Algorithms • Exclusive Clustering • Data belongs to only one definite cluster • Eg. K-Means Algorithms • Overlapping Clustering • Uses fuzzy set for clustering • Single Data may belong to one or more clusters • Hierarchical Clustering • Agglomerative(Bottom-up) • Divisive(Top-Down)
- 10. K-Means Algorithm Minimization of Squared Error Function
- 11. Demo
- 12. Here we have a dataset!
- 13. We randomly choose 2 group centroids!
- 14. We assign each point to the group that has the closest centroit.
- 15. We recalculate the positions of the centroids.
- 16. We assign each point to the group that has the closest centroid.
- 17. We recalculate the positions of the centroids.
- 18. Other Approaches
- 19. Hierarchical clustering • Agglomerative (bottom up) 1. start with 1 point (singleton) 2. recursively add two or more appropriate clusters 3. Stop when k number of clusters is achieved.
- 20. Hierarchical clustering • Divisive (Top down) 1. Start with a big cluster 2. Recursively divide into smaller clusters 3. Stop when k number of clusters is achieved.
- 21. References • www-users.cs.umn.edu/~kumar/.../chap8_basic_cluster_analysis.pdf • http://en.wikipedia.org/wiki/Cluster_analysis • https://www.cs.duke.edu/courses/fall03/cps260/notes/lecture18.pdf • http://www.matlab- cookbook.com/recipes/0100_Statistics/150_kmeans_clustering.html • http://www.cs.utah.edu/~germain/PPS/Topics/Matlab/plot.html • http://www.mathworks.com/help/stats/k-means-clustering.html