A model of mining approximate frequent itemsets using rough set theory
by Xiaomei Yu; Jun Zhao; Hong Wang; Xiangwei Zheng; Xiaoyan Yan
International Journal of Computational Science and Engineering (IJCSE), Vol. 19, No. 1, 2019

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.

Online publication date: Mon, 20-May-2019

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