A novel approach for mining maximal frequent patterns. (1st May 2017)

• Record Type: Journal Article
• Authors: Vo, Bay; Pham, Sang; Le, Tuong; Deng, Zhi-Hong
• Is Part Of: Expert systems with applications. Volume 73(2017)
• Page Start: 178
• Page End: 186
• Publication Date: 2017-05-01
• Subjects: Data mining -- Pattern mining -- Maximal frequent patterns -- N-list structure -- Pruning technique; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.12.023
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: An N-list structure is used to compress the dataset for mining Maximal Frequent Patterns. A pruning technique is then proposed and used in INLA-MFP algorithm to improve the runtime and memory usage. Experiments were conducted to show that INLA-MFP outperforms well-known algorithms. Abstract: Mining maximal frequent patterns (MFPs) is an approach that limits the number of frequent patterns (FPs) to help intelligent systems operate efficiently. Many approaches have been proposed for mining MFPs, but the complexity of the problem is enormous. Therefore, the run time and memory usage are still large. Recently, the N-list structure has been proposed and verified to be very effective for mining FPs, frequent closed patterns, and top-rank- k FPs. Therefore, this paper uses the N-list structure for mining MFPs. A pruning technique is also proposed to prune branches to reduce the search space. This technique is applied to an algorithm called INLA-MFP (improved N-list-based algorithm for mining maximal frequent patterns) for mining MFPs. Experiments were conducted to evaluate the effectiveness of the proposed algorithm. The experimental results show that INLA-MFP outperforms two state-of-the-art algorithms for mining MFPs.