Efficient approach for incremental weighted erasable pattern mining with list structure

• Published in: Expert systems with applications Vol. 143; p. 113087
• Main Authors: Nam, Hyoju, Yun, Unil, Yoon, Eunchul, Lin, Jerry Chun-Wei
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.04.2020; Elsevier BV
• Subjects: Algorithms; Data mining; Datasets; Erasable patterns; Incremental mining; Pattern analysis; Pruning techniques; Run time (computers); Weighted conditions; Erasable patterns; Pruning techniques; Weighted conditions; Data mining; Incremental mining
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.113087

•Efficient Incremental Weighted Erasable Pattern Mining is suggested.•List structures for incremental weighted erasable patterns are proposed.•Pruning techniques considering the list structures are presented.•Performance improvements are shown with various experiments. Erasable pattern mining is one of the important fields of frequent pattern mining. It diagnoses and solves the economic problems that arise in the manufacturing industry. The real-world database is continually accumulated over time, and each item has a different importance. Therefore, if we use conventional erasable pattern mining without considering the characteristics of the real-world database, less meaningful patterns can be extracted. Also, when mining a real-world database, the algorithm must be able to process operations quickly and efficiently. In this paper, in order to meet these requirements, we propose an algorithm which is implemented as a list structure for mining erasable patterns in an incremental database with weighted condition. Compared to existing state-of-the-art mining algorithms, the proposed algorithm performs pattern pruning by applying weighted condition to a dynamic database, so it extracts fewer candidate patterns and shows fast performance. We test our algorithms and the algorithms previously presented with various real datasets and synthetic datasets and obtained results such as run time, memory usage, scalability, and accuracy tests. By analyzing and comparing these experimental results, we show that the proposed algorithm has outstanding performance.