Efficient Discovery of Fuzzy Partial Periodic Frequent Patterns Within Quantitative Temporal Databases

Partial periodic patterns play a significant role in identifying regularities within temporal databases. However, most existing research has focused on discovering these patterns in binary datasets, overlooking the critical insights about the quantitative values associated with the items. This study utilizes the principles of fuzzy sets to propose a novel model for discovering fuzzy partial periodic frequent patterns (FPPFPs) within a quantitative temporal database. A robust depth-first search algorithm has also been proposed to uncover all FPPFPs. The proposed algorithm incorporates a novel pruning strategy that effectively reduces the search space and the computational cost required for discovering the FPPFPs. The experimental findings on synthetic and real-world datasets demonstrate the efficiency of the proposed algorithm. Finally, a case study utilizing air pollution data is provided to showcase the practical applicability and significance of the identified patterns.