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How the following Algorithms work
• Clustering
• Collaborative filtering : recommender systems
• Multidimensional scaling
...
Esclusive clustering
Alg.clustering
• Version partitional clustering (Hartigan’s algorithm)
• Version k-mean (random initi...
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 t...
Applications
• Amazon : reccomending articles to users
• Facebook : reccomending friends
• Netflix : reccomending movies
•...
Multidimensional Scaling
Multidimensional Scaling 1
Multidimensional scaling 2
-16
-15.5
-15
-14.5
-14
-13.5
-13
-12.5
-14 -12 -10 -8 -6 -4 -2 0
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 + K...
Dimesionality reduction3: app
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Algorithms presentation

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algorithms of Clustering, collaboarative filtering, multidimensional scaling and PCA

Publié dans : Sciences
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Algorithms presentation

  1. 1. How the following Algorithms work • Clustering • Collaborative filtering : recommender systems • Multidimensional scaling • PCA (Principal Component Analysis)
  2. 2. Esclusive clustering Alg.clustering • Version partitional clustering (Hartigan’s algorithm) • Version k-mean (random initialization)
  3. 3. Versione partitional clustering (Hartigan’s alg.)
  4. 4. K-Means
  5. 5. Applications.
  6. 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. 7. Applications • Amazon : reccomending articles to users • Facebook : reccomending friends • Netflix : reccomending movies • Google : recomending .. anything 
  8. 8. Multidimensional Scaling
  9. 9. Multidimensional Scaling 1
  10. 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. 11. Multidimensional scaling 3: app
  12. 12. Dimensionality reduction • PCA (Principal Component Analysis): eigenvectors decomposition. • JAMA: Java Matrix library
  13. 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. 14. Dimesionality reduction3: app

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