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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
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