An Algebraic Approach to Clustering and Classification with Support Vector Machiness

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dc.contributor.authorArslan, Güvenç
dc.contributor.authorMadran, Uğur
dc.contributor.authorSoyoğlu, Duygu
dc.date.accessioned2025-01-21T16:26:47Z
dc.date.available2025-01-21T16:26:47Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractIn this note, we propose a novel classification approach by introducing a new clustering method, which is used as an intermediate step to discover the structure of a data set. The proposed clustering algorithm uses similarities and the concept of a clique to obtain clusters, which can be used with different strategies for classification. This approach also reduces the size of the training data set. In this study, we apply support vector machines (SVMs) after obtaining clusters with the proposed clustering algorithm. The proposed clustering algorithm is applied with different strategies for applying SVMs. The results for several real data sets show that the performance is comparable with the standard SVM while reducing the size of the training data set and also the number of support vectors. © 2022 by the authors Licensee MDPI, Basel, Switzerland.
dc.identifier.doi10.3390/math10010128
dc.identifier.issn2227-7390
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85122986042
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/math10010128
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23204
dc.identifier.volume10
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofMathematics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectAlgebraic statistics; Classification; Clique; Clustering; Machine learning; Support vector machine
dc.titleAn Algebraic Approach to Clustering and Classification with Support Vector Machiness
dc.typeArticle

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