Local Boundary Fuzzified Rough K-Means-Based Information Granulation Algorithm Under the Principle of Justifiable Granularity

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dc.authoridYue, Dong/0000-0001-7810-9338
dc.authoridpeng, chen/0000-0003-3652-2233
dc.authoridZhang, Tengfei/0000-0002-2503-7024
dc.authoridZhang, Yudi/0009-0000-1224-3016
dc.authorwosidYue, Dong/ITW-1908-2023
dc.authorwosidpeng, chen/HHS-8720-2022
dc.authorwosidZhang, Tengfei/R-4485-2018
dc.contributor.authorZhang, Tengfei
dc.contributor.authorZhang, Yudi
dc.contributor.authorMa, Fumin
dc.contributor.authorPeng, Chen
dc.contributor.authorYue, Dong
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:42:42Z
dc.date.available2024-05-19T14:42:42Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractInformation granularity and information granules are fundamental concepts that permeate the entire area of granular computing. With this regard, the principle of justifiable granularity was proposed by Pedrycz, and subsequently a general two-phase framework of designing information granules based on Fuzzy C-means clustering was successfully developed. This design process leads to information granules that are likely to intersect each other in substantially overlapping clusters, which inevitably leads to some ambiguity and misperception as well as loss of semantic clarity of information granules. This limitation is largely due to imprecise description of boundary-overlapping data in the existing algorithms. To address this issue, the rough k-means clustering is introduced in an innovative way into Pedrycz's two-phase information granulation framework, together with the proposed local boundary fuzzy metric. To further strengthen the characteristics of support and inhibition of boundary-overlapping data, an augmented parametric version of the principle is refined. On this basis, a local boundary fuzzified rough k-means-based information granulation algorithm is developed. In this manner, the generated granules are unique and representative whilst ensuring clearer boundaries. The validity and performance of this algorithm are demonstrated through the results of comparative experiments.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [62073173, 61973151, 61833011]; Key Research and Development Plan of Jiangsu Province, China [BE2021001-4]; Qing Lan Project of Jiangsu Province, Chinaen_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 62073173, Grant 61973151, and Grant 61833011; in part by the Key Research and Development Plan of Jiangsu Province, China under Grant BE2021001-4; and in part by the Qing Lan Project of Jiangsu Province, China.en_US
dc.identifier.doi10.1109/TCYB.2023.3257274
dc.identifier.endpage532en_US
dc.identifier.issn2168-2267
dc.identifier.issn2168-2275
dc.identifier.issue1en_US
dc.identifier.pmid37030830en_US
dc.identifier.scopus2-s2.0-85180409970en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage519en_US
dc.identifier.urihttps://doi.org10.1109/TCYB.2023.3257274
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5274
dc.identifier.volume54en_US
dc.identifier.wosWOS:000966951900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Cyberneticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectInformation Granularityen_US
dc.subjectLocal Boundary Fuzzy Metricen_US
dc.subjectOverlapping Dataen_US
dc.subjectPrinciple Of Justifiable Granularityen_US
dc.subjectRough K-Means Clusteringen_US
dc.titleLocal Boundary Fuzzified Rough K-Means-Based Information Granulation Algorithm Under the Principle of Justifiable Granularityen_US
dc.typeArticleen_US

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