Mining Fuzzy Association Rules Based on Parallel Particle Swarm Optimization Algorithm

作者：Gou Jin^*; Wang Fei; Luo Wei

来源：Intelligent Automation and Soft Computing, 2015, 21(2): 147-162.

DOI：10.1080/10798587.2014.957482

• Fuzzy association rules
• Multi-mutation
• Particle swarm optimization
• Task allocation plan
• Multi-swarm parallel

The association rule extraction process often involves a large number of candidate item sets and multiple read operations on data sets. With the emergence of massive data, the sequential association rule extraction algorithm also suffers from large I/O overhead and insufficient memory. This paper presents a new multi-swarm parallel multi-mutation particle swarm optimization algorithm (MsP-MmPSO) to search several groups in parallel. Experimental results show that the MsP-MmPSO algorithm has an advantage in terms of execution time over traditional particle swarm optimization, especially when the amount or dimensions of the data increase. Experiments also verify that a good task allocation method can reduce the execution time of the parallel algorithm.

• 出版日期2015-4-3
• 单位华侨大学