Segmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualization

Basit Öğe Kaydı
dc.authoridDOĞAN, ONUR / 0000-0003-3543-4012
dc.authoridÖztaysi, Başar / 0000-0002-1090-7963
dc.authoridFernandez-Llatas, Carlos / 0000-0002-2819-5597
dc.authorscopusid57202924825
dc.authorscopusid8572344300
dc.authorscopusid57204151458
dc.authorwosidDoğan, Onur/AAN-3208-2021
dc.authorwosidDOĞAN, ONUR/ABI-4575-2020
dc.authorwosidFernandez-Llatas, Carlos/B-2957-2013
dc.authorwosidÖztaysi, Başar/K-7498-2013
dc.contributor.authorDoğan, Onur
dc.contributor.authorÖztayşi, Başar
dc.contributor.authorFernandez-Llatas, Carlos
dc.date.accessioned2022-02-15T16:57:33Z
dc.date.available2022-02-15T16:57:33Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.description13th International FLINS Conference on Uncertainity Modeling in Knowledge Engineering and Decision Making (FLINS) -- AUG 21-24, 2018 -- Belfast, NORTH IRELANDen_US
dc.description.abstractThere are some studies and methods in the literature to understand customer needs and behaviors from the path. However, path analysis has a complex structure because the many customers can follow many different paths. Therefore, clustering methods facilitate the analysis of the customer location data to evaluate customer behaviors. Therefore, we aim to understand customer behavior by clustering their paths. We use an intuitionistic fuzzy c-means clustering (IFCM) algorithm for two-dimensional indoor customer data; case durations and the number of visited locations. Customer location data was collected by Bluetooth-based technology devices from one of the major shopping malls in Istanbul. Firstly, we create customer paths from customer location data by using process mining that is a technique that can be used to increase the understandability of the IFCM results. Moreover, we show with this study that fuzzy methods and process mining technique can be used together to analyze customer paths and gives more understandable results. We also present behavioral changes of some customers who have a different visit by inspecting their clustered paths.en_US
dc.description.sponsorshipFLINSen_US
dc.identifier.doi10.3233/JIFS-179440
dc.identifier.endpage684en_US
dc.identifier.issn1064-1246
dc.identifier.issn1875-8967
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85078348795en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage675en_US
dc.identifier.urihttps://doi.org/10.3233/JIFS-179440
dc.identifier.urihttps://hdl.handle.net/20.500.14034/204
dc.identifier.volume38en_US
dc.identifier.wosWOS:000506856200067en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIOS Pressen_US
dc.relation.journalJournal Of Intelligent & Fuzzy Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy c-means clusteringen_US
dc.subjectintuitionistic fuzzy setsen_US
dc.subjectprocess miningen_US
dc.subjectcustomer behaviorsen_US
dc.subjectindoor locationsen_US
dc.subjectBluetooth Trackingen_US
dc.subjectBehavioren_US
dc.subjectTrajectoriesen_US
dc.subjectAlgorithmen_US
dc.titleSegmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualizationen_US
dc.typeConference Objecten_US

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