Efficient strategies for spatial data clustering using topological relations

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorNguyen, Trang T. D.
dc.contributor.authorNguyen, Loan T. T.
dc.contributor.authorBui, Quang-Thinh
dc.contributor.authorDuy, Le Nhat
dc.contributor.authorPedrycz, Witold
dc.contributor.authorVo, Bay
dc.date.accessioned2025-04-17T14:35:55Z
dc.date.available2025-04-17T14:35:55Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractUsing topology in data analysis is a promising new field, and recently, it has attracted numerous researchers and played a vital role in both research and application. This study explores the burgeoning field of topology-based data analysis, mainly focusing on its application in clustering algorithms within data mining. Our research addresses the critical challenges of reducing execution time and enhancing clustering quality, which includes decreasing the dependency on input parameters - a notable limitation in current methods. We propose five innovative strategies to optimize clustering algorithms that utilize topological relationships by combining solutions of expanding points fewer times, merging clusters, and using a jump to increase the radius value according to the nearest neighbor distance array index. These strategies aim to refine clustering performance by improving algorithmic efficiency and the quality of clustering outcomes. This approach elevates the standard of cluster analysis and contributes significantly to the evolving landscape of data mining and analysis.
dc.identifier.citationNguyen, T. T., Nguyen, L. T., Bui, Q. T., Duy, L. N., Pedrycz, W., & Vo, B. (2025). Efficient strategies for spatial data clustering using topological relations. Applied Intelligence, 55(2), 203.
dc.identifier.doi10.1007/s10489-024-05927-8
dc.identifier.endpage26
dc.identifier.issn0924-669X
dc.identifier.issn1573-7497
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85212933260
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1007/s10489-024-05927-8
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6303
dc.identifier.volume55
dc.identifier.wosWOS:001383333200003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofApplied intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectNetwork Spatial Analysis
dc.subjectSpatial Clustering
dc.subjectTopological Relations
dc.subjectTopological-Based Clustering
dc.titleEfficient strategies for spatial data clustering using topological relations
dc.typeArticle

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