Performance comparison of K-means and DBSCAN methods for airline customer segmentation

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dc.authorid0000-0002-8076-3678
dc.contributor.authorŞahinbaş, Kevser
dc.date.accessioned2023-01-27T10:04:17Z
dc.date.available2023-01-27T10:04:17Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractOrganizations are now fully embracing ideas such as customer success, customer loyalty, customer experience management and customer satisfaction. The application of these concepts must be based on three pillars of technology, process and people, to ensure that the organization ultimately has satisfied, loyal and successful customers. In today's competitive environment, as in all sectors, gaining great services in the aviation industry can provide a competitive advantage. With this study, it is aimed to help aviation companies to know how their services should meet the needs of customers and to obtain passenger satisfaction. Customer segmentation is widely used, which groups objects according to the similarity difference on each object and provides a high level of homogeneity in the same cluster or a high level of heterogeneity between each group. The aim of this study is to examine airline passenger satisfaction by using data mining methods including K-Means and DBSCAN clustering algorithms to reveal the service quality importance for customer satisfaction. K-Means algorithm achieved better results than DBSCAN algorithm with a Silhouette value of 0.1450671.
dc.identifier.citationŞahinbaş, K. (2022). Performance comparison of K-means and DBSCAN methods for airline customer segmentation. Black Sea Journal of Engineering and Science, 5(4), 158-165. https://dx.doi.org/10.34248/bsengineering.1170943
dc.identifier.doi10.34248/bsengineering.1170943
dc.identifier.endpage165
dc.identifier.issn2619-8991
dc.identifier.issue4
dc.identifier.startpage158
dc.identifier.trdizinid1132011
dc.identifier.urihttps://dx.doi.org/10.34248/bsengineering.1170943
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10386
dc.identifier.volume5
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorŞahinbaş, Kevser
dc.language.isoen
dc.publisherUğur Şen
dc.relation.ispartofBlack Sea Journal of Engineering and Scienceen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClustering
dc.subjectCustomer Segmentation
dc.subjectK-Means
dc.subjectDBSCAN
dc.subjectData Mining
dc.subjectData Management
dc.titlePerformance comparison of K-means and DBSCAN methods for airline customer segmentation
dc.typeArticle

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