Computing Rule Confidence Using Rough Set and Data Mining
Rough Set theory is a new mathematical tool to deal with representation, learning, vagueness, uncertainty and generalization of knowledge. It has been used in machine learning, knowledge discovery, decision support system s and pattern recognition. It can abstract underlying rules from data. Confidence is the criterion to scaling the reliability of rules. Traditionally, the algorithm to obtain the deduction of decision rule in rough sets theory always take more into account of the number of decision rules than the cost of the rules.