Recursive Queried Frequent Patterns Algorithm: Determining Frequent Pattern Sets from Database

Abstract

Frequent pattern mining is a fundamental method for Data Mining, applicable in market basket analysis, recommendation systems, and academic analytics. Widely adopted and foundational algorithms such as Apriori and FP-Growth, which represent the standard approaches in frequent pattern mining, face limitations related to candidate set generation and memory usage, especially when applied to extensive relational datasets. This work presents the Recursive Queried Frequent Patterns (RQFP) algorithm, an SQL-based approach that utilizes recursive queries on relational Mining Tables to detect frequent itemsets without the need for explicit candidate development. The algorithm was implemented using a Microsoft SQL Server and demonstrated through a custom-developed C# web application interface. RQFP facilitates easy integration with database systems and enhances result interpretability. Comparative analyses of Apriori and FP-Growth on an academic dataset reveal competitive efficacy, accompanied with diminished memory requirements and enhanced clarity in pattern extraction. The paper further contextualizes RQFP using benchmark datasets from the previous literature and delineates a roadmap for future evaluations in healthcare and retail data. The existing implementation is educational, although the technique demonstrates the potential for scalable, database-native pattern mining.