Theoretical comparison of Relational Concept Analysis (RCA) and Graph-FCA (GCA)

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2025-06-05 DOI:10.1016/j.ijar.2025.109496

Vanessa Fokou , Peggy Cellier , Xavier Dolques , Sébastien Ferré , Florence Le Ber

引用次数: 0

Abstract

Relational Concept Analysis (RCA) and Graph-FCA (GCA) are two extensions of Formal Concept Analysis (FCA) introduced in order to allow concept analysis on multi-relational data. The two methods have different properties and parameters, but when restricting to binary relationships, existential quantifier and unary concepts, their outputs look similar. On this basis, a theoretical comparison of the two methods is conducted, showing that each RCA concept corresponds to a GCA concept. Furthermore, to allow the comparison of concept intensions, a transformation of RCA results into relational patterns is performed. These results give a sound basis to help interpreting RCA results and to combine the two approaches for data exploration.

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关系概念分析（RCA）与图形fca （GCA）的理论比较

关系概念分析（RCA）和图形概念分析（GCA）是形式概念分析（FCA）的两个扩展，目的是允许对多关系数据进行概念分析。这两种方法具有不同的属性和参数，但是当限制为二元关系、存在量词和一元概念时，它们的输出看起来很相似。在此基础上，对两种方法进行了理论比较，表明每个RCA概念对应一个GCA概念。此外，为了允许概念内涵的比较，将RCA结果转换为关系模式。这些结果为帮助解释RCA结果和将两种方法结合起来进行数据探索提供了坚实的基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Approximate Reasoning 工程技术-计算机：人工智能

CiteScore

6.90

自引率

12.80%

发文量

170

审稿时长

67 days

期刊介绍： The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.