A model of mining approximate frequent itemsets using rough set theory. (2nd May 2019)

• Record Type: Journal Article
• Authors: Yu, Xiaomei; Zhao, Jun; Wang, Hong; Zheng, Xiangwei; Yan, Xiaoyan
• Is Part Of: International journal of computational science and engineering. Volume 19:Number 1(2019)
• Page Start: 71
• Page End: 82
• Publication Date: 2019-05-02
• Subjects: rough set theory -- RST -- data mining -- decision table -- approximate frequent itemsets -- AFIs -- indiscernibility relation; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Datasets can be described by decision tables. In real-life applications, data are usually incomplete and uncertain, which presents big challenges for mining frequent itemsets in imprecise databases. This paper presents a novel model of mining approximate frequent itemsets using the theory of rough sets. With a transactional information system constructed on the dataset under consideration, a transactional decision table is put forward, then lower and upper approximations of support are available which can be easily computed from the indiscernibility relations. Finally, by a divide-and-conquer way, the approximate frequent itemsets are discovered taking consideration of support-based accuracy and coverage defined. The evaluation of the novel model is conducted on both synthetic datasets and real-life applications. The experimental results demonstrate its usability and validity.