A weighted N-list-based method for mining frequent weighted itemsets

• Published in: Expert systems with applications 2018-04, Vol.96, p.388-405
• Main Authors: Bui, Huong, Vo, Bay, Nguyen, Ham, Nguyen-Hoang, Tu-Anh, Hong, Tzung-Pei
• Format: Article
• Language: English
• Subjects: Data mining; Expert systems; Frequent weighted itemsets; Medical treatment; Theft; Theorems; Weighted N-list; Weighted support
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.10.039

•Weighted N-list structure is developed.•Theorems 3, 4 and 6 are proposed to fast calculate the weighted support of itemsets.•Theorem 5 is proposed reduce the time complexity.•NFWI algorithm is built based on these theorems for efficiently mining frequent weighted itemsets.•The proposed method is efficient than the existing methods, especially when run on very large databases. Mining frequent itemsets (FIs) is an important problem in the field of data mining, and thus there have been many different methods proposed to solve this problem. However, mining FIs usually works on binary databases and has a limitation that is only concerned with the appearance of items regardless of their importance. In practical applications, items often have different importance depending on their values or meanings, and that leads to the emergence of weighted databases. In this paper, we propose a new method for mining frequent weighted itemsets (FWIs) from a weighted database by using the weighted N-list structure (WN-list), an extension of the N-list. Some theorems are proposed to calculate the weighted supports of itemsets fast, and then an algorithm is built based on these theorems for efficiently mining FWIs. The experimental results show that the proposed method outperforms existing methods, especially when run on very large and sparse databases.