From: edpurcell@gmail.com   
      
   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.   
      
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