An overall framework of modeling, clustering, and evaluation for trapezoidal information granules

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / FPE-7309-2022
dc.contributor.authorTang, Yiming
dc.contributor.authorGao, Jianwei
dc.contributor.authorPedrycz, Witold
dc.contributor.authorXi, Lei
dc.contributor.authorRen, Fuji
dc.date.accessioned2025-04-18T09:56:12Z
dc.date.available2025-04-18T09:56:12Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn existing granular clustering algorithms, the design of coverage and specificity does not fully capture the inherent structural characteristics of granular data together with the optimization issue, and the current weight setting for the granular data is not sufficient. To address these problems, in this study, the trapezoidal information granule, which is rarely studied before, is concentrated, and we come up with a novel granular clustering algorithm called the weighted possibilistic fuzzy c-means algorithm for trapezoidal granularity (WPFCM-T). First, under the acknowledged principle of justifiable granularity, novel functions of coverage and specificity are designed for trapezoidal information granules, considering the internal characteristics of such granules. The idea of particle swarm optimization (PSO) is exploited to upgrade the established granular data, and then the trapezoidal information granule construction (TIGC) method is proposed to realize granular modeling. Second, an exponential weight is constructed with regard to coverage and specificity, while a novel distance via $\alpha$-cuts is given. The possibilistic fuzzy c-means structure is introduced into granular clustering, in which the new weight and distance are integrated, resulting in the proposed WPFCM-T algorithm. Third, the RC is studied to evaluate granular clustering, and hence an overall framework including granular modeling, clustering, and evaluation is constructed. Finally, through experiments completed on artificial datasets, UCI datasets, large datasets, high-dimensional datasets, and noisy datasets, WPFCM-T has superior granular data reconstruction ability by contrast with other granular clustering algorithms, indicating that the granular clustering performance of WPFCM-T is better than the others.
dc.description.sponsorshipNational Natural Science Foundation of China (NSFC)
dc.identifier.citationTang, Y., Gao, J., Pedrycz, W., Xi, L., & Ren, F. (2024). An Overall Framework of Modeling, Clustering and Evaluation for Trapezoidal Information Granules. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3376328
dc.identifier.endpage3496
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85187978508
dc.identifier.scopusqualityQ1
dc.identifier.startpage3484
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3376328
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6896
dc.identifier.volume32
dc.identifier.wosWOS:001240137400037
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherIEEE-INST electrical electronics engineers
dc.relation.ispartofIEEE transactions on fuzzy systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClustering Algorithms
dc.subjectFuzzy Sets
dc.subjectData Models
dc.subjectNumerical Models
dc.subjectHeuristic Algorithms
dc.subjectOptimization
dc.subjectGranular Computing
dc.subjectFuzzy Clustering
dc.subjectPrinciple of Justifiable Granularity
dc.subjectTrapezoidal Information Granule
dc.titleAn overall framework of modeling, clustering, and evaluation for trapezoidal information granules
dc.typeArticle

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