Knowledge-Induced Multiple Kernel Fuzzy Clustering

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dc.authoridTang, Yiming/0000-0002-0917-2277
dc.authoridHu, Xianghui/0000-0001-7137-2416
dc.authorwosidTang, Yiming/AAL-6708-2020
dc.contributor.authorTang, Yiming
dc.contributor.authorPan, Zhifu
dc.contributor.authorHu, Xianghui
dc.contributor.authorPedrycz, Witold
dc.contributor.authorChen, Renhao
dc.date.accessioned2024-05-19T14:40:11Z
dc.date.available2024-05-19T14:40:11Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe introduction of domain knowledge opens new horizons to fuzzy clustering. Then knowledge-driven and data-driven fuzzy clustering methods come into being. To address the challenges of inadequate extraction mechanism and imperfect fusion mode in such class of methods, we propose the Knowledge-induced Multiple Kernel Fuzzy Clustering (KMKFC) algorithm. First, to extract knowledge points better, the Relative Density-based Knowledge Extraction (RDKE) method is proposed to extract high-density knowledge points close to cluster centers of real data structure, and provide initialized cluster centers. Moreover, the multiple kernel mechanism is introduced to improve the adaptability of clustering algorithm and map data to high-dimensional space, so as to better discover the differences between the data and obtain superior clustering results. Second, knowledge points generated by RDKE are integrated into KMKFC through a knowledge-influence matrix to guide the iterative process of KMKFC. Third, we also provide a strategy of automatically obtaining knowledge points, and thus propose the RDKE with Automatic knowledge acquisition (RDKE-A) method and the corresponding KMKFC-A algorithm. Then we prove the convergence of KMKFC and KMKFC-A. Finally, experimental studies demonstrate that the KMKFC and KMKFC-A algorithms perform better than thirteen comparison algorithms with regard to four evaluation indexes and the convergence speed.en_US
dc.description.sponsorshipNational Key Research and Development Program of China [2020YFC1523100]; National Natural Science Foundation of China [62176083, 62277014, 62176084, 61976078]en_US
dc.description.sponsorshipThis work was supported in part by the National Key Research and Development Program of China under Grant 2020YFC1523100, and in part by the National Natural Science Foundation of China under Grants 62176083, 62277014, 62176084, and 61976078.en_US
dc.identifier.doi10.1109/TPAMI.2023.3298629
dc.identifier.endpage14855en_US
dc.identifier.issn0162-8828
dc.identifier.issn1939-3539
dc.identifier.issue12en_US
dc.identifier.pmid37490382en_US
dc.identifier.scopus2-s2.0-85165881556en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage14838en_US
dc.identifier.urihttps://doi.org10.1109/TPAMI.2023.3298629
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4920
dc.identifier.volume45en_US
dc.identifier.wosWOS:001104973300049en_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 Pattern Analysis and Machine Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectCluster Center Initializationen_US
dc.subjectClusteringen_US
dc.subjectFuzzy C-Meansen_US
dc.subjectMultiple Kernelen_US
dc.titleKnowledge-Induced Multiple Kernel Fuzzy Clusteringen_US
dc.typeArticleen_US

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