From: yuanxi80@hotmail.com   
      
   Hi,   
      
   I have got a problem at hand. It is a sales forecasting problem. In   
   the data set both input and output variables are continuous valued.   
   The input variables describe the characteristics of a supermarket   
   store for example. They could be the store location, store size, range   
   of items held, and so on. The output variable is an indicator of sales   
   for example, number of £'s per month. Say the sales figure is between   
   £0.00 and £1000.00. Say I group the values of output variable into   
   different intervals, say, 'low', 'medium' and 'high'. Then is it   
   possible to induce classification rules from the continuous data in   
   the following way using a regression tree.   
      
   Regression Data --> Regression Tree --> Regression Rules -->   
   Discretization --> Classification Rules   
      
   The discretization step groups the values of output variable into the   
   intervals, 'low', 'medium' and 'high'.   
      
   Are there any standard/popular approaches for this type of problems?   
   Are fuzzy regression trees suitable for this type of problems? If   
   there are standard approaches, may I have some suggestions of the   
   relevant/key papers/references in this area?   
      
   Regards,   
      
   David   
      
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