Two improved N-two-stage K-means clustering aggregation algorithmic paradigms for HFLTS possibility distributions

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dc.authoridChen, Zhen-Song/0000-0003-4360-5459
dc.authoridXin, Yaojiao/0009-0005-2654-0683
dc.authoridRodriguez, Rosa Maria/0000-0002-1736-8915
dc.authorwosidChen, Zhen-Song/K-3436-2019
dc.authorwosidRodriguez, Rosa Maria/B-9618-2011
dc.contributor.authorXiong, Sheng-Hua
dc.contributor.authorXin, Yao-Jiao
dc.contributor.authorChen, Zhen-Song
dc.contributor.authorRodriguez, Rosa M.
dc.contributor.authorFeng, Si-Hai
dc.contributor.authorMartinez, Luis
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:42:22Z
dc.date.available2024-05-19T14:42:22Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe available method based on statistical principles for aggregating hesitant fuzzy linguistic term set (HFLTS) possibility distribution is the N-two-stage algorithmic aggregation paradigm driven by the K-means clustering (N2S-KMC). Nonetheless, the N2S-KMC method is subject to two significant limitations. (i) The grouping technique is capable of effectively partitioning decision-making information into N groups. However, it does not determine the appropriate placement of members within each group, as the number of computations is dependent on the number of elements present in each group, rather than the elements themselves. (ii) The initial clustering centers of K-means clustering are chosen without adhering to the distribution law within the aggregated hesitant 2-tuple linguistic terms set (H2TLTS) possibility distribution. This may result in a reduction in the clustering performance. In order to address the aforementioned limitations, we suggest two enhancement techniques for the former. Firstly, we propose the utilization of the minimum average difference (MAD) method to ascertain the number of groups. This approach aims to reduce the time required for the initial stage of aggregation following grouping. Secondly, we recommend the implementation of the maximize compactness degree of inter-group grouping (MCDIGG) method. This method enables the identification of group members, resulting in a more concentrated distribution of data subsequent to grouping. The present study suggests the utilization of MAD and MCDIGG techniques as a substitute for the grouping approach in the N2S-KMC model. This leads to the development of a new algorithm, IN2S-DO-KMC, wherein the data is partitioned into K subsets in a descending order to determine the initial center for KMC. Furthermore, with respect to the issue present in the subsequent phase, we propose the utilization of the density canopy (DC) algorithm to perform pre-clustering of the data and produce the initial clustering center and the quantity of clusters for the K- means algorithm. Subsequently, a refined version of the N2S-KMC model, denoted as IN2S-DC-KMC, has been suggested. Ultimately, an empirical study is conducted to assess the validity and practicability of the proposed framework for evaluating failure modes in medical devices. The outcomes are evaluated with regards to the efficacy of the algorithm, the numerical dispersion, and the pragmatic ramifications.en_US
dc.description.sponsorshipLaboratory of Civil Aircraft Fire Science and Safety Engineering, China [202275]; Civil Aviation Safety Capacity Construction Foundation [20212023]; Higher Education Talent Cultivation Quality and Teaching Reform Project of Sichuan Province [JG2021-316, E2022053]; Special Funds for Higher Education Reform of the Central Government [J2018-12]; Civil Avi-ation Flight University of China Scientific Research Foundation [J2020-112, 72171182]; National Natural Science Foundation of China [71971182, 72031009, PGC2018-099402-B-I00]; Spanish Government projecten_US
dc.description.sponsorshipThis work was supported by Civil Aviation Education Talent Foundation [grant No. MHJY2022013]; Autonomous Project of Sichuan Key & nbsp;Laboratory of Civil Aircraft Fire Science and Safety Engineering, China [grant No. MZ2022JB03] ; Civil Aviation Safety Capacity Construction Foundation [grant No. 202275] ; Higher Education Talent Cultivation Quality and Teaching Reform Project of Sichuan Province, 2021-2023, China [grant No. JG2021-316] ; Special Funds for Higher Education Reform of the Central Government [grant No. E2022053] ; Civil Aviation Flight University of China Scientific Research Foundation, [grant No. J2018-12, J2020-112] , and partly by the National Natural Science Foundation of China (Grant nos. 72171182, 71971182, and 72031009) and the Spanish Government project (Grant no. PGC2018-099402-B-I00) .r Laboratory of Civil Aircraft Fire Science and Safety Engineering, China [grant No. MZ2022JB03] ; Civil Aviation Safety Capacity Construction Foundation [grant No. 202275] ; Higher Education Talent Cultivation Quality and Teaching Reform Project of Sichuan Province, 2021-2023, China [grant No. JG2021-316] ; Special Funds for Higher Education Reform of the Central Government [grant No. E2022053] ; Civil Avi-ation Flight University of China Scientific Research Foundation, [grant No. J2018-12, J2020-112] , and partly by the National Natural Science Foundation of China (Grant nos. 72171182, 71971182, and 72031009) and the Spanish Government project (Grant no. PGC2018-099402-B-I00) .en_US
dc.identifier.doi10.1016/j.inffus.2023.101964
dc.identifier.issn1566-2535
dc.identifier.issn1872-6305
dc.identifier.scopus2-s2.0-85167820780en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.inffus.2023.101964
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5234
dc.identifier.volume100en_US
dc.identifier.wosWOS:001062822600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofInformation Fusionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectHesitant Fuzzy Linguistic Term Setsen_US
dc.subjectK-Means Clusteringen_US
dc.subjectInformation Fusionen_US
dc.subjectComputing With Wordsen_US
dc.subjectAggregation Paradigmen_US
dc.titleTwo improved N-two-stage K-means clustering aggregation algorithmic paradigms for HFLTS possibility distributionsen_US
dc.typeArticleen_US

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