{"title":"利用先进的web本体语言属性派生出新颖一致的关联规则","authors":"Eliot Bytyçi, Lule Ahmedi","doi":"10.1504/ijbidm.2023.134321","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":35458,"journal":{"name":"International Journal of Business Intelligence and Data Mining","volume":"135 1","pages":"411-429"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using advanced web ontology language properties for deriving novel and consistent association rules\",\"authors\":\"Eliot Bytyçi, Lule Ahmedi\",\"doi\":\"10.1504/ijbidm.2023.134321\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":35458,\"journal\":{\"name\":\"International Journal of Business Intelligence and Data Mining\",\"volume\":\"135 1\",\"pages\":\"411-429\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Business Intelligence and Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijbidm.2023.134321\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Decision Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Business Intelligence and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijbidm.2023.134321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Decision Sciences","Score":null,"Total":0}
期刊介绍：
IJBIDM provides a forum for state-of-the-art developments and research as well as current innovative activities in business intelligence, data analysis and mining. Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks. IJBIDM highlights intelligent techniques used for business modelling, including all areas of data visualisation, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, data mining techniques, tools and applications, neurocomputing, evolutionary computing, fuzzy techniques, expert systems, knowledge filtering, and post-processing. Topics covered include Data extraction/reporting/cleaning/pre-processing OLAP, decision analysis, causal modelling Reasoning under uncertainty, noise in data Business intelligence cycle Model specification/selection/estimation Web technology, mining, agents Fuzzy, neural, evolutionary approaches Genetic algorithms, machine learning, expert/hybrid systems Bayesian inference, bootstrap, randomisation Exploratory/automated data analysis Knowledge-based analysis, statistical pattern recognition Data mining algorithms/processes Classification, projection, regression, optimisation clustering Information extraction/retrieval, human-computer interaction Multivariate data visualisation, tools.
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