Accelerated Fuzzy C-Means Clustering Based on New Affinity Filtering and Membership Scaling

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dc.authoridLi, Dong/0000-0002-7104-1921
dc.authoridZhou, Shuisheng/0000-0003-4764-9483
dc.authorwosidLi, Dong/JTS-3361-2023
dc.contributor.authorLi, Dong
dc.contributor.authorZhou, Shuisheng
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:41:42Z
dc.date.available2024-05-19T14:41:42Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractFuzzy C-Means (FCM) is a widely used clustering method. However, FCM and its many accelerated variants have low efficiency in the mid-to-late stage of the clustering process. In this stage, all samples are involved in updating their non-affinity centers, and the membership grades of most samples, whose assignments remain unchanged, are still updated by calculating the sample-center distances. All these factors lead to the algorithms converging slowly. In this paper, a new affinity filtering technique is developed to recognize a complete set of non-affinity centers for each sample with low computations. Then, a new membership scaling technique is suggested to set the membership grades between each sample and its non-affinity centers to 0 and maintain the fuzzy membership grades for others. By integrating these two techniques, FCM based on new affinity filtering and membership scaling (AMFCM) is proposed to accelerate the whole convergence process of FCM. Numerous experimental results performed on synthetic and real-world data sets have shown the feasibility and efficiency of the proposed algorithm. Compared with state-of-the-art algorithms, AMFCM is significantly faster and more effective. For example, AMFCM reduces the number of FCM iterations by 80% on average.en_US
dc.description.sponsorshipMinistry of Investments and European Projects through the Human Capital Sectoral Operational Program 2014-2020 [153735, 62461/03.06.2022]en_US
dc.description.sponsorshipThis work was supported in part by the Ministry of Investments and European Projects through the Human Capital Sectoral Operational Program 2014-2020 (SMIS code 153735) under Contract 62461/03.06.2022.en_US
dc.identifier.doi10.1109/TKDE.2023.3273274
dc.identifier.endpage12349en_US
dc.identifier.issn1041-4347
dc.identifier.issn1558-2191
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85162877746en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage12337en_US
dc.identifier.urihttps://doi.org10.1109/TKDE.2023.3273274
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5148
dc.identifier.volume35en_US
dc.identifier.wosWOS:001105152100032en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee Computer Socen_US
dc.relation.ispartofIeee Transactions on Knowledge and Data Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectFuzzy C-Meansen_US
dc.subjectAffinity Filteringen_US
dc.subjectTriangle Inequalityen_US
dc.subjectNon-Affinity Centeren_US
dc.subjectNon-Affinity Sampleen_US
dc.subjectMembership Scalingen_US
dc.titleAccelerated Fuzzy C-Means Clustering Based on New Affinity Filtering and Membership Scalingen_US
dc.typeArticleen_US

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