Adaptive weights clustering of research papers

Larisa Adamyan (), Kirill Efimov (), Cathy Y. Chen () and Wolfgang Härdle
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Larisa Adamyan: Humboldt-Universität zu Berlin, C.A.S.E.-Center of Applied Statistics and Economics
Kirill Efimov: Humboldt-Universität zu Berlin, C.A.S.E.-Center of Applied Statistics and Economics
Cathy Y. Chen: Humboldt-Universität zu Berlin, C.A.S.E.-Center of Applied Statistics and Economics

Digital Finance, 2020, vol. 2, issue 3, No 1, 169-187

Abstract: Abstract The JEL classification system is a standard way of assigning key topics to economic articles to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of the Collaborative Research Center from Humboldt-Universität zu Berlin we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on http://www.quantlet.de/ and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with $$k$$ k -means or CLUTO reveals excellent performance of AWC.

Keywords: Clustering; JEL system; Adaptive algorithm; Economic articles; Nonparametric; C02; C14; C45; C63; C87 (search for similar items in EconPapers)
Date: 2020
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Working Paper: Adaptive weights clustering of research papers (2017)
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DOI: 10.1007/s42521-020-00017-z

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