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Maximum Likelihood Dataclustering

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Authors: Matteo Marsili and Lorenzo Giada

Email: marsili@sissa.it, lgiada@sissa.it

Home Page: http://www.sissa.it/~marsili/http://www.mpikg-golm.mpg.de/th/people/lgiada/

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Table of Contents of the presentation

  1. Maximum likelyhood data clustering with application to financial time series and gene expression data 
  2. Data clustering 
  3. Standard approaches 
  4. Non standard approaches 
  5. Maximum likely-hood approach 
  6. Data sets 
  7. The model 
  8. Maximum likely-hood solution 
  9. Notes 
  1. Financial Data 
  2. Results S&P500 I 
  3. Results S&P500 II 
  4. Localization of objects in clusters 
  5. Noise undressing 
  6. Gene expression data 
  7. Gene expression technologies 
  8. Results 
  9. Comparison with other methods 
  10. Conclusions