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

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

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Nombre: Dogan;Oztays;Fernández ...
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dc.contributor.author Dogan, Onur es_ES
dc.contributor.author Oztaysi, Basar es_ES
dc.contributor.author Fernández Llatas, Carlos es_ES
dc.date.accessioned 2021-06-29T03:31:31Z
dc.date.available 2021-06-29T03:31:31Z
dc.date.issued 2020-01-09 es_ES
dc.identifier.issn 1064-1246 es_ES
dc.identifier.uri http://hdl.handle.net/10251/168485
dc.description.abstract [EN] There 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. es_ES
dc.language Inglés es_ES
dc.publisher IOS Press es_ES
dc.relation.ispartof Journal of Intelligent & Fuzzy Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Fuzzy c-means clustering es_ES
dc.subject Intuitionistic fuzzy sets es_ES
dc.subject Process mining es_ES
dc.subject Customer behaviors es_ES
dc.subject Indoor locations es_ES
dc.title Segmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualization es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.3233/JIFS-179440 es_ES
dc.rights.accessRights Cerrado es_ES
dc.description.bibliographicCitation Dogan, O.; Oztaysi, B.; Fernández Llatas, C. (2020). Segmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualization. Journal of Intelligent & Fuzzy Systems. 38(1):675-684. https://doi.org/10.3233/JIFS-179440 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 13th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making es_ES
dc.relation.conferencedate Agosto 21-24,2018 es_ES
dc.relation.conferenceplace Belfast, North Ireland es_ES
dc.relation.publisherversion https://doi.org/10.3233/JIFS-179440 es_ES
dc.description.upvformatpinicio 675 es_ES
dc.description.upvformatpfin 684 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 38 es_ES
dc.description.issue 1 es_ES
dc.relation.pasarela S\403336 es_ES
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