Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2650
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dc.contributor.authorAksaç, Alper-
dc.contributor.authorÖzyer, Tansel-
dc.contributor.authorAlhajj, Reda-
dc.date.accessioned2019-12-25T14:01:59Z
dc.date.available2019-12-25T14:01:59Z
dc.date.issued2019-12
dc.identifier.citationAksac, A., Özyer, T., and Alhajj, R. (2019). CutESC: Cutting Edge Spatial Clustering Technique based on Proximity Graphs. Pattern Recognition.en_US
dc.identifier.issn0031-3203
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0031320319302468?via%3Dihub-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2650-
dc.description.abstractIn this paper, we propose a cut-edge algorithm for spatial clustering (CutESC) based on proximity graphs. The CutESC algorithm removes edges when a cut-edge value for the edge's endpoints is below a threshold. The cut-edge value is calculated by using statistical features and spatial distribution of data based on its neighborhood. Also, the algorithm works without any prior information and preliminary parameter settings while automatically discovering clusters with non-uniform densities, arbitrary shapes, and outliers. However, there is an option which allows users to set two parameters to better adapt clustering solutions for particular problems. To assess advantages of CutESC algorithm, experiments have been conducted using various two-dimensional synthetic, high-dimensional real-world, and image segmentation datasets. Published by Elsevier Ltd.en_US
dc.language.isoenen_US
dc.publisher Elsevier Ltden_US
dc.relation.ispartofPattern recognitionen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSpatial data miningen_US
dc.subjectclusteringen_US
dc.subjectproximity graphsen_US
dc.subjectgraph theoryen_US
dc.titleCutESC: Cutting edge spatial clustering technique based on proximity graphsen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Artificial Intelligence Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümütr_TR
dc.identifier.volume96
dc.identifier.wosWOS:000487569700005en_US
dc.identifier.scopus2-s2.0-85068847294en_US
dc.institutionauthorÖzyer, Tansel-
dc.contributor.YOKid143116-
dc.identifier.doi10.1016/j.patcog.2019.06.014-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.grantfulltextnone-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering
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