最小基数还是直接基数？这就是问题所在！

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

International Journal of Approximate Reasoning Pub Date : 2025-06-11 DOI:10.1016/j.ijar.2025.109509

Jaume Baixeries , Amedeo Napoli

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引用次数: 0

摘要

在本文中，我们重新讨论了计算一组属性的闭包的问题。这个问题在逻辑学、关系数据库模型、格理论和形式概念分析中都很重要。依赖关系的基础可能具有不同的特征，其中包括“最小”的，例如dg基础，或者是“直接”的，例如规范直接单元基础和d基础。在这里，我们提出了一个广泛的和实验研究的影响极小性和直接对闭包算法。在真实数据集和合成数据集上进行的实验结果进行了深入的分析，并提出了一种不同的、全新的观点，即计算一组属性的闭包。

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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A minimal base or a direct base? That is the question!

In this paper we revisit the problem of computing the closure of a set of attributes given a basis of dependencies or implications. This problem is of main interest in logics, in the relational database model, in lattice theory, and in Formal Concept Analysis as well. A basis of dependencies may have different characteristics, among which being “minimal”, e.g., the DG-Basis, or being “direct”, e.g., the Canonical-Direct Unit Basis and the D-base. Here we propose an extensive and experimental study of the impacts of minimality and directness on the closure algorithms. The results of the experiments performed on real and synthetic datasets are analyzed in depth, and suggest a different and fresh look at computing the closure of a set of attributes w.r.t. a basis of dependencies.

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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.