A genetic algorithm-based framework for mining quantitative association rules without specifying minimum support and minimum confidence

Discovering association rules is a useful and common technique for data mining in which relations and co-dependencies between datasets are shown. One of the most important challenges of data mining is to discover the rules of continuous numerical datasets. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the support and confidence criteria. In this paper a multi-objective algorithm for mining quantitative association rules is proposed. The procedure is based on the Genetic Algorithm, and there is no need there is no need to determine the extent of the threshold for the support and confidence criteria. By proposing a multi-criteria method, the useful and attractive rules and the most suitable numerical intervals are discovered, without the need to discrete numerical values and the determination of the minimum support threshold and minimum confidence threshold. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness, and cosine2. The results obtained from real-world datasets demonstrate the effectiveness of the proposed approach. The algorithm is used to examine three datasets and the results show the performance superiority of the proposed algorithm compared to similar algorithms.