CutESC: Cutting edge spatial clustering technique based on proximity graphs

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dc.authorid0000-0001-6657-9738
dc.contributor.authorAksaç, Alper
dc.contributor.authorÖzyer, Tansel
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2019-12-19T08:04:44Z
dc.date.available2019-12-19T08:04:44Z
dc.date.issued2019
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
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.
dc.identifier.citationAksaç, A., Özyer, T. ve Alhajj, R. (2019). CutESC: Cutting edge spatial clustering technique based on proximity graphs. Pattern Recognition, 96. https://doi.org/10.1016/j.patcog.2019.06.014
dc.identifier.doi10.1016/j.patcog.2019.06.014
dc.identifier.issn0031-3203
dc.identifier.issn1873-5142
dc.identifier.issue96
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.patcog.2019.06.014
dc.identifier.urihttps://hdl.handle.net/20.500.12511/4538
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofPattern Recognitionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectSpatial Data Mining
dc.subjectClustering
dc.subjectProximity Graphs
dc.subjectGraph Theory
dc.titleCutESC: Cutting edge spatial clustering technique based on proximity graphs
dc.typeArticle

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