From: milind.a.joshi@gmail.com   
      
   Dmitry A. Kazakov wrote:   
   > On Mon, 17 Jul 2006 01:45:32 GMT, Dephased wrote:   
   >   
   > > I have a dataset of observations (about 100 000 observations). Each   
   > > observation gives me the state of  30 discrete variables at a given   
   > > time.   
   > >   
   > > I would like to know if there exists any "distance" that could tell me   
   > > "how far" an observation is from another? I am not trying to get the   
   > > distance between two variables but rather between two "vectors" which   
   > > are made of the observations of 30 different variables at a given time.   
   > >   
   > > I read up a bit on the subject but I must admit I am confused with all   
   > > the possible measures and what they achieve: chi square, euclidean,   
   > > mahalanobis...   
   >   
   > This is the central issue of machine learning. If the distance in the   
   > feature space (observation vectors) where known, then you could easily   
   > classify any observation just by using nearest neighbour search.   
   >   
   > Unfortunately there is no one distance for all possible spaces. Just   
   > consider that some observations in fact depend on others.   
   >   
   > --   
   > Regards,   
   > Dmitry A. Kazakov   
   > http://www.dmitry-kazakov.de   
   >   
      
   Hi,   
   A good way of knowing which "distance measure" is the best, is to   
   calculate various distances, and see which ones seems to fit best   
   intuitively, and then figure out how you can translate your intuition   
   to a formula!   
      
   Regards,   
   Milind   
      
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