SSC: Clustering of Turkish Texts By Spectral Graph Partitioning

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dc.authoriduckan, Taner/0000-0001-5385-6775
dc.authoridKarci, Ali/0000-0002-8489-8617
dc.authorwosiduckan, Taner/IZP-9705-2023
dc.authorwosidKarci, Ali/AAG-5337-2019
dc.contributor.authorUckan, Taner
dc.contributor.authorHark, Cengiz
dc.contributor.authorKarci, Ali
dc.date.accessioned2024-08-04T20:11:43Z
dc.date.available2024-08-04T20:11:43Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
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.en_US
dc.identifier.doi10.2339/politeknik.684558
dc.identifier.endpage1444en_US
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue4en_US
dc.identifier.startpage1433en_US
dc.identifier.trdizinid1235229en_US
dc.identifier.urihttps://doi.org/10.2339/politeknik.684558
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1235229
dc.identifier.urihttps://hdl.handle.net/11616/92955
dc.identifier.volume24en_US
dc.identifier.wosWOS:000762330700011en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isotren_US
dc.publisherGazi Univen_US
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGraph partitioningen_US
dc.subjectspectral graph theoryen_US
dc.subjectbinary text clusteringen_US
dc.subjecttext categorizationen_US
dc.subjecttext miningen_US
dc.titleSSC: Clustering of Turkish Texts By Spectral Graph Partitioningen_US
dc.typeArticleen_US

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