A novel parallel framework for metaheuristic-based frequent itemset mining

Djenouri, Youcef; Djenouri, Djamel; Belhadi, Asma; Chun-Wei Lin, Jerry; Bendjoudi, Ahcene; Fournier-Viger, Philippe

Abstract

Frequent Itemset Mining (FIM) is an important but very time-consuming data mining task. As a result, traditional FIM algorithms are often not scalable to large databases. To address this issue, several metaheuristics have been developed in recent years to find good approximate solutions to the FIM problem. It was shown that such approaches can be much more efficient than exact algorithms. However, metaheuristics often have long runtimes on massive datasets and the quality of their solutions can be improved. To address this issue, this paper proposes a parallel framework called CFIM (Cluster for Frequent Itemset Mining) for metaheuristic-based FIM. It accelerates FIM by using multiple cluster workers. The proposed approach partitions a transactional database and the set of all items at the level of cluster workers. The itemset generation process is performed by each worker, which then send results to a master node. This latter performs a merging step to only keep high quality itemsets by considering their frequency and diversification. Three metaheuristics (GA, PSO and BSO) are integrated in this framework to yield three novel metaheuristics (CGA, CPSO and CBSO). Extensive experiments show that CPSO outperforms CGA, CBSO, and state-of-the-art high performance computing FIM approaches.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2019 IEEE Congress on Evolutionary Computation (CEC)
• Start Date: Jun 10, 2019
• End Date: Jun 13, 2019
• Acceptance Date: Mar 8, 2019
• Online Publication Date: Aug 8, 2019
• Publication Date: 2019
• Deposit Date: Apr 22, 2021
• Pages: 1439-1445
• ISBN: 9781728121536
• DOI: https://doi.org/10.1109/cec.2019.8790116
• Public URL: https://uwe-repository.worktribe.com/output/7283580