Stream Convolution for Attribute Reduction of Concept Lattices

Jianfeng Xu, Chenglei Wu, Jilin Xu, Lan Liu () and Yuanjian Zhang ()
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Jianfeng Xu: School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
Chenglei Wu: School of Software, Nanchang University, Nanchang 330047, China
Jilin Xu: School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
Lan Liu: School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
Yuanjian Zhang: China UnionPay Co., Ltd., Shanghai 201201, China

Mathematics, 2023, vol. 11, issue 17, 1-19

Abstract: Attribute reduction is a crucial research area within concept lattices. However, the existing works are mostly limited to either increment or decrement algorithms, rather than considering both. Therefore, dealing with large-scale streaming attributes in both cases may be inefficient. Convolution calculation in deep learning involves a dynamic data processing method in the form of sliding windows. Inspired by this, we adopt slide-in and slide-out windows in convolution calculation to update attribute reduction. Specifically, we study the attribute changing mechanism in the sliding window mode of convolution and investigate five attribute variation cases. These cases consider the respective intersection of slide-in and slide-out attributes, i.e., equal to, disjoint with, partially joint with, containing, and contained by. Then, we propose an updated solution of the reduction set for simultaneous sliding in and out of attributes. Meanwhile, we propose the CLARA-DC algorithm, which aims to solve the problem of inefficient attribute reduction for large-scale streaming data. Finally, through the experimental comparison on four UCI datasets, CLARA-DC achieves higher efficiency and scalability in dealing with large-scale datasets. It can adapt to varying types and sizes of datasets, boosting efficiency by an average of 25%.

Keywords: concept lattice; attribute reduction; convolution calculation; streaming computing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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