Extracting attribute implications from a formal context: Unifying the basic approaches

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2024-09-02 DOI:10.1016/j.ins.2024.121419

引用次数: 0

Abstract

There have been several pioneering approaches to the extraction of attribute implications from a formal context, dating from the 1980's: the one of Guigues and Duquenne based on so-called non-redundancy nodes, another one proposed by Ganter highlighting the concept of pseudo-closed set, and the best-known one relying on the recursive computation of so-called pseudo-intents in the book by Ganter and Wille. The Guigues and Duquenne approach has never been compared in detail in the literature with the other two, although they turn out to be equivalent. This paper tries to fill this gap, proposing a unified view, hopefully more easy to grasp.

查看原文本刊更多论文

从形式语境中提取属性含义：统一基本方法

自 20 世纪 80 年代以来，从形式语境中抽取属性含义的方法有几种开创性的方法：Guigues 和 Duquenne 提出的基于所谓非冗余节点的方法、Ganter 提出的强调伪封闭集概念的另一种方法，以及 Ganter 和 Wille 著作中最著名的依赖于所谓伪意图递归计算的方法。Guigues 和 Duquenne 方法与其他两种方法虽然等价，但从未在文献中进行过详细比较。本文试图填补这一空白，提出一种统一的观点，希望更易于理解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

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.