Fuzzy association rule mining approach to identify e-commerce product association considering sales amount

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dc.authoridDogan, Onur/0000-0003-3543-4012
dc.authorwosidDogan, Onur/HPC-1959-2023
dc.contributor.authorDoğan, Onur
dc.contributor.authorKem, Furkan Can
dc.contributor.authorÖztayşi, Başar
dc.date.accessioned2023-03-22T19:47:28Z
dc.date.available2023-03-22T19:47:28Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractOnline stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers' choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey) [3180641]en_US
dc.description.sponsorshipThis work has been financially supported by TUBITAK (The Scientific and Technological Research Council of Turkey), Project Number: 3180641.en_US
dc.identifier.doi10.1007/s40747-021-00607-3
dc.identifier.endpage1560en_US
dc.identifier.issn2199-4536
dc.identifier.issn2198-6053
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85132399265en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1551en_US
dc.identifier.urihttps://doi.org/10.1007/s40747-021-00607-3
dc.identifier.urihttps://hdl.handle.net/20.500.14034/720
dc.identifier.volume8en_US
dc.identifier.wosWOS:000738565700014en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.journalComplex & Intelligent Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectE-commerceen_US
dc.subjectRecommendation systemen_US
dc.subjectFuzzy rulesen_US
dc.subjectAssociation rule miningen_US
dc.subjectFuzzy set theoryen_US
dc.subjectOnlineen_US
dc.subjectTypologyen_US
dc.subjectModelen_US
dc.titleFuzzy association rule mining approach to identify e-commerce product association considering sales amounten_US
dc.typeArticleen_US

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