From: clearking@gmail.com   
      
   On Apr 13, 12:54 pm, DrColombes  wrote:   
   > Probabilistic likelihood "distance" functions compute well with   
   > missing or multiple observations of real-valued functions, but how   
   > should a metric distance function (e.g., Euclidean distance) handle   
   > missing or multiple observations?   
   >   
   > Assuming a worst-case difference for missing attributes would seem to   
   > dilute the discrimination ability of the observed attributes, and   
   > averaging multiple observations would seem to reduce the contribution   
   > of multiple observations.   
   >   
   > Thanks for your comments, suggestions.   
   >   
      
   is it ok that you use the attribute value of its nearest neighbor to   
   estimate it? the computation of the neighbor is based on the attribute   
   values it has.   
      
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