Discovery of fuzzy quantitative sequential patterns with multiple minimum supports and adjustable membership functions

The objective of mining quantitative sequential patterns is to discover complete sets of sequential patterns with quantities in databases. Although this novel type of pattern considers information, compared to traditional sequential patterns, it contains a sharp boundary problem; that is, when the quantity of an item is near the boundary of two predetermined quantity intervals, it can be either ignored or overemphasized. Hong et al. proposed a new type of pattern, called a fuzzy quantitative sequential pattern (FQSP), to solve this problem. Although this type of pattern provides summary information to expedite decision making, FQSPs confront unrealistic circumstances, that is, they offer only a unique minimum support (min_sup) to all items for the whole database. A higher min_sup fails to find rare items with higher profit, whereas a lower min_sup leads to a combinatorial explosion problem. Instead of this type of mechanism, we used the idea of multiple minimum supports to mine an FQSP. Furthermore, the utility of FQSP is also disturbed because this approach uses only a single membership function without considering the price–quantity relation. The idea of adjustable membership functions was also provided to address this problem. This study proposes a new model to discover an FQSP with both multiple minimum supports and adjustable membership functions. Experiments using synthetic and real datasets demonstrated the computational efficiency, scalability, and effectiveness of the model.