三向概念格构造与关联规则获取

IF 6 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2025-05-01 Epub Date: 2025-01-08 DOI:10.1016/j.ins.2024.121867
Junping Xie , Jing Yang , Jinhai Li , Mingwei He , Huaxiang Song
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引用次数: 0

摘要

在三向概念分析中,如何快速构造对象诱导的三向概念格,获取三向决策关联规则是值得研究的问题。基于此,本研究的主要工作如下:首先,我们提出了一种新的快速建立对象诱导三向概念格的算法,包括快速生成对象诱导三向概念和建立这些概念之间的偏序,并通过实验验证了该算法的高效性。其次,定义了比双向决策关联规则表达更丰富知识的三向决策关联规则,探索了三向决策关联规则与双向决策关联规则之间的关系,提出了一种基于对象诱导的三向概念格提取三向决策关联规则的新算法。最后,我们将所提出的算法应用于交通事故原因分析,以彻底识别交通事故的耦合因素。
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Three-way concept lattice construction and association rule acquisition
In three-way concept analysis, how to quickly construct object-induced three-way concept lattices and acquire three-way decision association rules deserves to be studied. Based on this, the main work is done in this study as follows. Firstly, we raise a novel fast algorithm of setting up object-induced three-way concept lattices, which includes quickly generating object-induced three-way concepts and establishing the partial order among these concepts, and carry out experiments to verify the high efficiency of the algorithm. Secondly, we define three-way decision association rules, which can express richer knowledge than two-way decision association rules, explore the relationship between three-way decision association rules and two-way decision association rules, and give a new algorithm to extract three-way decision association rules grounded on object-induced three-way concept lattices. Finally, we apply the proposed algorithms to cause analysis of traffic accidents for thoroughly identifying the coupling factors of traffic accidents.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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