A clustering approach for classifying scholars based on publication performance using bibliometric data

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dc.authoridPeker, Serhat/0000-0002-6876-3982
dc.authoridPisirgen, Ali/0000-0001-7257-2938
dc.contributor.authorPisirgen, Ali
dc.contributor.authorPeker, Serhat
dc.date.accessioned2025-03-20T09:51:10Z
dc.date.available2025-03-20T09:51:10Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractThis study introduces a clustering framework that effectively evaluate scholars' publication performance by utilizing cluster analysis and bibliometric data. In order to capture the various aspects of scholars' publication characteristics, our proposed framework integrates four distinct features, namely APIR which represents Academic age, Productivity, Impact, and Recency. The proposed framework is implemented in a case study focusing on Turkish academia, utilizing a dataset comprising 13,070 scholars from 24 diverse academic divisions across 30 Turkish universities. Cluster analysis yields seven groups of scholars with diverse publishing characteristic based on APIR features and these obtained clusters are profiled as freshmen, stagnant impactful mids, rising stars, stagnant and non-prolific juniors, stagnant impactful seniors, super stars, currently active and prolific seniors. To enhance the cluster analysis results, additional cross analysis is performed based on scholars' certain demographics such as affiliating institutes, divisions, academic titles, and PhD qualification. Scholars in clusters with superior publication performance are often affiliated with top-ranked universities and have academic backgrounds in the fields of Medicine, Engineering, and Natural Sciences. Practically, generated scholar segments and analysis based on these scholar profiles can serve as useful input for policy makers during having decisions about recruitment, promotion, awarding and allocation of funds.
dc.identifier.doi10.1016/j.eij.2024.100537
dc.identifier.issn1110-8665
dc.identifier.issn2090-4754
dc.identifier.scopus2-s2.0-85204446726
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.eij.2024.100537
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2454
dc.identifier.volume28
dc.identifier.wosWOS:001322210200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherCairo Univ, Fac Computers & Information
dc.relation.ispartofEgyptian Informatics Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectData mining
dc.subjectCluster analysis
dc.subjectBibliometric indicators
dc.subjectScholar profile
dc.subjectScholar-level evaluation
dc.subjectPublication performance
dc.titleA clustering approach for classifying scholars based on publication performance using bibliometric data
dc.typeArticle

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