A scalable association rule learning heuristic for large datasets

Abstract

Abstract Many algorithms have proposed to solve the association rule learning problem. However, most of these algorithms suffer from the problem of scalability either because of tremendous time complexity or memory usage, especially when the dataset is large and the minimum support (minsup) is set to a lower number. This paper introduces a heuristic approach based on divide-and-conquer which may exponentially reduce both the time complexity and memory usage to obtain approximate results that are close to the accurate results. It is shown from comparative experiments that the proposed heuristic approach can achieve significant speedup over existing algorithms.

Keywords

• Association rule learning
• Frequent itemset mining
• Scalability
• Graph partitioning
• Apriori algorithm
• FP-Growth algorithm