Generating of derivative membership functions for fuzzy association rule mining by Particle Swarm Optimization

The association Rule Mining (ARM) is a data mining task that extracts relations between items based on the item's frequency. The ARM regards items with high frequency as more interesting than items with low frequency. In quantitative datasets, each item will be grouped into a large range of values. Therefore, items with low frequencies may not be considered as interesting. Hence, the possibility of extracting potentially interesting relations between these items will decrease. Thus, this deficiency brings a challenging issue to this field. Most of the existing methods for quantitative ARM in handling this problem are based on the Sharp Boundary Discretization methods and Clustering methods. These methods group each item into intervals with crisp boundaries which do not overlap. They bring some problems as well, such as ignoring or emphasizing more on values near the boundary of intervals. To deal with the problem of quantitative ARM, the combination of S and Z fuzzy shapes, which is combined with the Particle Swarm Optimization (PSO) is proposed in this paper to generate appropriate membership functions for each item. Fuzzy logic will group items into overlapping intervals and then, the fuzzy rules will be generated from the interesting items. The performances of the proposed methods are evaluated over Bilkent datasets and then, are compared with the results of clustering method (Fuzzy C-Means) in aspect of their capability to transform data to fuzzy data and then their efficiency are evaluated based on the quality of their generated rules. The results show the efficiency of the proposed method to extract the rules with more quality.