Fuzzy Mining Algorithm for Extracting Membership Functions by Using Genetic Algorithm

Extracting association rules from transaction data is one of the most important purposes of data mining. In this paper, an enhanced Genetic- Fuzzy algorithm to derive membership functions dynamically from quantitative transaction data has been proposed. In this approach, selection pressure technique is used to adjust membership functions and fuzzify transaction data. Some experiments are also made to compare the proposed algorithm and existing approach. The results showed that the efficiency of proposed algorithm is better than the existing one