Input: Training set ( and are the sets of all positive and negative example, respectively)
Output: A set of rules for predicting class labels for examples
Procedure CATW
 rules null
  while and
   find the best attribute value use the improved correlation measure combine tuple weight with attribute weight
   add to
   remove from all examples not satisfying
   remove from all examples not satisfying
  add to rules
  for each attribute at that is included in antecedent of in
  for each example in satisfying ’s body
   if then remove from
return rules
Algorithm 1: Classification based on both attribute value weight and tuple weight (CATW).