Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/1738
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dc.contributor.authorPérez-Alonso, Alain-
dc.contributor.authorBlanco, Ignacio J.-
dc.contributor.authorSerrano, José M.-
dc.contributor.authorGonzález-González, Luisa M.-
dc.date.accessioned2024-01-29T08:49:34Z-
dc.date.available2024-01-29T08:49:34Z-
dc.date.issued2021-03-31-
dc.identifier.citationPérez-Alonso, A., Blanco, I. J., Serrano, J. M., & González-González, L. M. (2021). Incremental maintenance of discovered fuzzy association rules. Fuzzy Optimization and Decision Making, 1-21.es_ES
dc.identifier.issn1568-4539es_ES
dc.identifier.otherhttps://doi.org/10.1007/s10700-021-09350-3es_ES
dc.identifier.urihttps://link.springer.com/article/10.1007/s10700-021-09350-3es_ES
dc.identifier.urihttps://hdl.handle.net/10953/1738-
dc.description.abstractFuzzy association rules (FARs) are a recognized model to study existing relations among data, commonly stored in data repositories. In real-world applications, transactions are continuously processed with upcoming new data, rendering the discovered rules information inexact or obsolete in a short time. Incremental mining methods arise to avoid re-runs of those algorithms from scratch by re-using information that is systematically maintained. These methods are useful for extracting knowledge in dynamic environments.However, executing the algorithms only to maintain previously discovered information creates inefficiencies in real-time decision support systems. In this paper, two active algorithms are proposed for incremental maintenance of previously discovered FARs, inspired by efficient methods for change computation. The application of a generic form of measures in these algorithms allows the maintenance of a wide number of metrics simultaneously. We also propose to compute data operations in real-time, in order to create a reduced relevant instance set. The algorithms presented do not discover new knowledge; they are just created to efficiently maintain valuable information previously extracted, ready for decision making. Experimental results on education data and repository data sets showthat our methods achieve a good performance. In fact, they can significantly improve traditional mining, incremental mining, and a naïve approach.es_ES
dc.description.sponsorshipIberoamerican Association of Postgraduate Universities (AUIP)es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.ispartofFuzzy Optimization and Decision Making (2021), 20, 429-449es_ES
dc.subjectFuzzy association ruleses_ES
dc.subjectIncremental maintenancees_ES
dc.subjectReal-time decision support systemses_ES
dc.subjectActive databaseses_ES
dc.titleIncrementalmaintenance of discovered fuzzy association ruleses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES
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