How the following Algorithms work

• Clustering

• Collaborative filtering : recommender systems

• Multidimensional scaling

• PCA (Principal Component Analysis)

Esclusive clusteringAlg.clustering

• Version partitional clustering (Hartigan’s algorithm)

• Version k-mean (random initialization)

Versione partitional clustering (Hartigan’s alg.)

K-Means

Applications.

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

Applications

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

Multidimensional Scaling

Multidimensional Scaling 1

Multidimensional scaling 2-12.5 -12 -11.5 -11 -10.5 -10 -9.5

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Series1

Multidimensional scaling 3: app

Dimensionality reduction

• PCA (Principal Component Analysis): eigenvectors decomposition.

• JAMA: Java Matrix library

Dimensionality reduction2 : app

• Eigenbehaviors: identifying structure in Routine.

• SNA: community affiliation

• PCA + Kmeans = Spectral Clustering: PCA continous sol. <=> discrete sol. k-means clustering

Dimesionality reduction3: app