PPQAR: Parallel PSO for quantitative association rule mining

Abstract

Mining quantitative association rules is one of the most important tasks in data mining and exists in many real-world problems. Many researches have proved that particle swarm optimization(PSO) algorithm is suitable for quantitative association rule mining (ARM) and there are many successful cases in different fields. However, the method becomes inefficient even unavailable on huge datasets. This paper proposes a parallel PSO for quantitative association rule mining(PPQAR). The parallel algorithm designs two methods, particle-oriented and data-oriented parallelization, to fit different application scenarios. Experiments were conducted to evaluate these two methods. Results show that particle-oriented parallelization has a higher speedup, and data-oriented method is more general on large datasets.