{"title":"广义凸壳的描述网格","authors":"Christophe Demko, Karell Bertet, Jean-François Viaud, Cyril Faucher, Damien Mondou","doi":"10.1016/j.ijar.2024.109269","DOIUrl":null,"url":null,"abstract":"<div><p>In this article, we present a new approach to tackle lattice generation for complex and heterogeneous data using the concept of convexity. This is a work that we have already carried out, albeit intuitively <span><span>[11]</span></span> where we proposed the <span>NextPriorityConcept</span> algorithm for generating a meet-semilattice of concepts based on suitable descriptions and strategies. Now, we revisit the essential properties of our description spaces using a stronger formalism based on the properties of closure operators.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"174 ","pages":"Article 109269"},"PeriodicalIF":3.2000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0888613X24001567/pdfft?md5=fff1b6ef063a4772686d8b74306ad002&pid=1-s2.0-S0888613X24001567-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Description lattices of generalised convex hulls\",\"authors\":\"Christophe Demko, Karell Bertet, Jean-François Viaud, Cyril Faucher, Damien Mondou\",\"doi\":\"10.1016/j.ijar.2024.109269\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this article, we present a new approach to tackle lattice generation for complex and heterogeneous data using the concept of convexity. This is a work that we have already carried out, albeit intuitively <span><span>[11]</span></span> where we proposed the <span>NextPriorityConcept</span> algorithm for generating a meet-semilattice of concepts based on suitable descriptions and strategies. Now, we revisit the essential properties of our description spaces using a stronger formalism based on the properties of closure operators.</p></div>\",\"PeriodicalId\":13842,\"journal\":{\"name\":\"International Journal of Approximate Reasoning\",\"volume\":\"174 \",\"pages\":\"Article 109269\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0888613X24001567/pdfft?md5=fff1b6ef063a4772686d8b74306ad002&pid=1-s2.0-S0888613X24001567-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Approximate Reasoning\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0888613X24001567\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Approximate Reasoning","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888613X24001567","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
In this article, we present a new approach to tackle lattice generation for complex and heterogeneous data using the concept of convexity. This is a work that we have already carried out, albeit intuitively [11] where we proposed the NextPriorityConcept algorithm for generating a meet-semilattice of concepts based on suitable descriptions and strategies. Now, we revisit the essential properties of our description spaces using a stronger formalism based on the properties of closure operators.
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.
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