Clustering interval and triangular granular data: modeling, execution, and assessment

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorTang, Yiming
dc.contributor.authorWu, Wenbin
dc.contributor.authorPedrycz, Witold
dc.contributor.authorGao, Jianwei
dc.contributor.authorHu, Xianghui
dc.contributor.authorDeng, Zhaohong
dc.contributor.authorChen, Rui
dc.date.accessioned2025-06-18T13:35:10Z
dc.date.available2025-06-18T13:35:10Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the previous Euclidean distance.
dc.description.sponsorshipNational Natural Science Foundation of China
dc.identifier.citationTang, Y., Wu, W., Pedrycz, W., Gao, J., Hu, X., Deng, Z., & Chen, R. (2024). Clustering Interval and Triangular Granular Data: Modeling, Execution, and Assessment. IEEE Transactions on Neural Networks and Learning Systems.
dc.identifier.doi10.1109/TNNLS.2024.3499996
dc.identifier.endpage15
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.pmid40030499
dc.identifier.scopus2-s2.0-85211045327
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1109/TNNLS.2024.3499996
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7327
dc.identifier.wosWOS:001372005800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of electrical and electronics engineers inc.
dc.relation.ispartofIEEE transactions on neural networks and learning systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFuzzy Clustering
dc.subjectGranular Computing
dc.subjectInformation Granules
dc.subjectReconstruction Standard
dc.titleClustering interval and triangular granular data: modeling, execution, and assessment
dc.typeArticle

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