Numeric Multi-Objective Rule Mining Using Simulated Annealing Algorithm

as a single objective one. Measures like support, confidence and other interestingness criteria which are used for evaluating a rule, can be thought of as different objectives of association rule mining problem. Support count is the number of records, which satisfies all the conditions that exist in the rule. This objective represents the accuracy of the rules extracted from the database. Confidence represents the proportion of records for which the prediction of the rule (or model in the case of a complete classification) is correct, and it is one of the most widely quoted measures of quality, especially in the context of complete classification. Interestingness measures how much interesting the rule is. Using these three measures as the objectives of rule mining problem, this article uses a Simulated Annealing algorithm to extract some useful and interesting rules from any Market-basket type databases. The experimental results show that the algorithm may be suitable for large and noisy datasets but don’t stay in local minimum. Keywords: Simulated Annealing, Numeric Association Rule Mining.