algorithms of Clustering, collaboarative filtering, multidimensional scaling and PCA

1. How the following Algorithms work
• Clustering
• Collaborative filtering : recommender systems
• Multidimensional scaling
• PCA (Principal Component Analysis)

2. Esclusive clustering
Alg.clustering
• Version partitional clustering (Hartigan’s algorithm)
• Version k-mean (random initialization)

3. Versione partitional clustering (Hartigan’s alg.)

4. K-Means

5. Applications.

6. Collaborative filtering
• Given a set of users (or more in general objects), and/or
preferences, forcast the behavior of the users.
• MovieLens dataset.
• Item based CF

7. Applications
• Amazon : reccomending articles to users
• Facebook : reccomending friends
• Netflix : reccomending movies
• Google : recomending .. anything 

8. Multidimensional Scaling

9. Multidimensional Scaling 1

10. Multidimensional scaling 2
-16
-15.5
-15
-14.5
-14
-13.5
-13
-12.5
-14 -12 -10 -8 -6 -4 -2 0
Series1

11. Multidimensional scaling 3: app

12. Dimensionality reduction
• PCA (Principal Component Analysis):
eigenvectors decomposition.
• JAMA: Java Matrix library

13. Dimensionality reduction2 : app
• Eigenbehaviors: identifying structure in
Routine.
• SNA: community affiliation
• PCA + Kmeans = Spectral Clustering: PCA
continous sol. <=> discrete sol. k-means
clustering

14. Dimesionality reduction3: app