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.

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)

8. Clustering Algorithms
Agglomerative Divisive

9. K-Means Algorithm

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