Modeling and Clustering of Parabolic Granular Data

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorTang, Yiming
dc.contributor.authorGao, Jianwei
dc.contributor.authorPedrycz, Witold
dc.contributor.authorHu, Xianghui
dc.contributor.authorXi, Lei
dc.contributor.authorRen, Fuji
dc.contributor.authorHu, Min
dc.date.accessioned2025-04-18T10:10:34Z
dc.date.available2025-04-18T10:10:34Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAt present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.
dc.identifier.citationTang, Y., Gao, J., Pedrycz, W., Hu, X., Xi, L., Ren, F., & Hu, M. (2024). Modeling and Clustering of Parabolic Granular Data. IEEE Transactions on Artificial Intelligence
dc.identifier.doi10.1109/TAI.2024.3377172
dc.identifier.endpage3742
dc.identifier.issn26914581
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85188464057
dc.identifier.scopusqualityQ1
dc.identifier.startpage3728
dc.identifier.urihttp://dx.doi.org/10.1109/TAI.2024.3377172
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6979
dc.identifier.volume5
dc.indekslendigikaynakScopus
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 Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClustering
dc.subjectFuzzy Clustering
dc.subjectFuzzy Set Theory
dc.subjectGranular Computing (GRC)
dc.subjectUnsupervised Learning
dc.titleModeling and Clustering of Parabolic Granular Data
dc.typeArticle

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