Application of k-means clustering algorithm to improve effectiveness of the results recommended by journal recommender system

This study investigates to evaluate feasibility of k-means clustering algorithm in order to improve effectiveness of the results recommended by RICEST Journal Finder System. More than 15,000 papers published in filed of engineering journals during 2013–2017 were collected from their websites. Their...

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Veröffentlicht in:	Scientometrics Jg. 127; H. 6; S. 3237 - 3252
Hauptverfasser:	Vara, Narjes, Mirzabeigi, Mahdieh, Sotudeh, Hajar, Fakhrahmad, Seyed Mostafa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.06.2022 Springer Nature B.V
Schlagworte:	Algorithms Attitudes Cluster analysis Clustering Computer Science Effectiveness Feasibility Feasibility studies Information Storage and Retrieval Library Science Recommender systems System effectiveness Vector quantization Websites Journal recommender systems k-means clustering algorithm RICeST journal finder system Effectiveness Scalability
ISSN:	0138-9130, 1588-2861
Online-Zugang:	Volltext
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