SSC: Clustering of Turkish Texts By Spectral Graph Partitioning

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dc.authoriduckan, Taner/0000-0001-5385-6775|KARCI, Ali/0000-0002-8489-8617;
dc.contributor.authorUckan, Taner
dc.contributor.authorHark, Cengiz
dc.contributor.authorKarci, Ali
dc.date.accessioned2025-10-24T18:09:39Z
dc.date.available2025-10-24T18:09:39Z
dc.date.issued2021
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThere is growing interest in studies on text classification as a result of the exponential increase in the amount of data available. Many studies have been conducted in the field of text clustering, using different approaches. This study introduces Spectral Sentence Clustering (SSC) for text clustering problems, which is an unsupervised method based on graph-partitioning. The study explains how the proposed model proposed can be used in natural language applications to successfully cluster texts. A spectral graph theory method is used to partition the graph into non-intersecting sub-graphs, and an unsupervised and efficient solution is offered for the text clustering problem by providing a physical representation of the texts. Finally, tests have been conducted demonstrating that SSC can be successfully used for text categorization. A clustering success rate of 97.08% was achieved in tests conducted using the TTC-3600 dataset, which contains open-access unstructured Turkish texts, classified into categories. The SSC model proposed performed better compared to a popular k-means clustering algorithm.
dc.identifier.doi10.2339/politeknik.684558
dc.identifier.endpage1444
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue4
dc.identifier.startpage1433
dc.identifier.trdizinid1235229
dc.identifier.urihttps://doi.org/10.2339/politeknik.684558
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1235229
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3762
dc.identifier.volume24
dc.identifier.wosWOS:000762330700011
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isotr
dc.publisherGazi Univ
dc.relation.ispartofJournal Of Polytechnic-Politeknik Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20251023
dc.subjectGraph partitioning; spectral graph theory; binary text clustering; text categorization; text mining
dc.titleSSC: Clustering of Turkish Texts By Spectral Graph Partitioning
dc.typeArticle

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