Robust clustering algorithm: The use of soft trimming approach

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dc.authoridTaheri, Sona/0000-0003-1779-4567
dc.contributor.authorTaheri, Sona
dc.contributor.authorBagirov, Adil M.
dc.contributor.authorSultanova, Nargiz
dc.contributor.authorOrdin, Burak
dc.date.accessioned2024-08-31T07:49:58Z
dc.date.available2024-08-31T07:49:58Z
dc.date.issued2024
dc.departmentEge Üniversitesien_US
dc.description.abstractThe presence of noise or outliers in data sets may heavily affect the performance of clustering algorithms and lead to unsatisfactory results. The majority of conventional clustering algorithms are sensitive to noise and outliers. Robust clustering algorithms often overcome difficulties associated with noise and outliers and find true cluster structures. We introduce a soft trimming approach for the hard clustering problem where its objective is modeled as a sum of the cluster function and a function represented as a composition of the algebraic and distance functions. We utilize the composite function to estimate the degree of the significance of each data point in clustering. A robust clustering algorithm based on the new model and a procedure for generating starting cluster centers is developed. We demonstrate the performance of the proposed algorithm using some synthetic and real-world data sets containing noise and outliers. We also compare its performance with that of some well-known clustering techniques. Results show that the new algorithm is robust to noise and outliers and finds true cluster structures.en_US
dc.description.sponsorshipAustralian Government through the Australian Research Council's Discovery Projects funding scheme [DP190100580]en_US
dc.description.sponsorshipThe research by Dr. Adil M. Bagirov is supported by the Australian Government through the Australian Research Council's Discovery Projects funding scheme (Project No. DP190100580) . The authors express their gratitude to three anonymous referees for their invaluable comments, which have contributed to enhancing the quality of the paper.en_US
dc.identifier.doi10.1016/j.patrec.2024.06.032
dc.identifier.endpage22en_US
dc.identifier.issn0167-8655
dc.identifier.issn1872-7344
dc.identifier.scopus2-s2.0-85198317120en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage15en_US
dc.identifier.urihttps://doi.org/10.1016/j.patrec.2024.06.032
dc.identifier.urihttps://hdl.handle.net/11454/105067
dc.identifier.volume185en_US
dc.identifier.wosWOS:001270713400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofPattern Recognition Lettersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240831_Uen_US
dc.subjectPartitional Clusteringen_US
dc.subjectRobust Clusteringen_US
dc.subjectIncremental Clusteringen_US
dc.subjectTrimming Approachen_US
dc.titleRobust clustering algorithm: The use of soft trimming approachen_US
dc.typeArticleen_US

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