An Efficient Algorithm for Fast Discovery of High-Efficiency Patterns

Abstract

The high-efficiency pattern mining (HEPM) problem has recently emerged as a variant of the high-utility pattern mining problem, aiming to identify patterns with the highest profit-to-investment ratio by considering both their utilities and investments. However, due to its vast search space, the HEPM problem is inherently difficult and complex to solve. Existing HEPM algorithms suffer from inefficiencies in runtime and memory usage due to inadequate search space pruning. This study introduces anew algorithm named EHEPM to address this issue more effectively. EHEPM introduces four new upper-bound models to enhance search space pruning and presents two data structures for the accurate and efficient calculation of pattern efficiency and upper-bound values. Experimental results conducted on various datasets demonstrate that EHEPM outperforms existing algorithms in terms of runtime, memory consumption, number of join operations, and scalability.