A cost-effective approach for mining near-optimal top-[formula omitted] patterns

Abstract

Frequent pattern mining (FPM) on large graphs has received more and more attention due to its importance in various applications, including social media analysis. The FPM models are designed to find the patterns in a large graph with a frequency above a user-defined threshold. However, this problem is nontrivial due to the mining process’s unaffordable computational and space costs. In light of this, we propose a cost-effective approach to mining near-optimal top-k patterns. Our approach applies a “level-wise” strategy to detect frequent patterns incrementally, hence can terminate once top-k patterns are discovered. Moreover, we develop a technique to compute the lower bound of support with a smart traverse strategy and compact data structures. Extensive experimental studies on real-life and synthetic graphs show that our approach performs well by outperforming traditional competing methods in efficiency, memory footprint, recall, and scalability.