Abstract
We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a “head subject” in the higher ranks of the taxonomy, that is supposed to “tightly” cover the query set, possibly bringing in some errors, both “gaps” and “offshoots”. Our hybrid method involves two more automated analysis techniques: a fuzzy clustering method, FADDIS, involving both additive and spectral properties, and a purely structural string-to-text relevance measure based on suffix trees annotated by frequencies. We apply this to extract research tendencies from two collections of research papers: (a) about 18000 research papers published in Springer journals on data science for 20 years, and (b) about 27000 research papers retrieved from Springer and Elsevier journals in response to data science related queries. We consider a taxonomy of Data Science based on the Association for Computing Machinery Classification of Computing System (ACM-CCS 2012). Our findings allow us to make some comments on the tendencies of research that cannot be derived by using more conventional techniques.
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Acknowledgments
D.F., A.V. and B.M. acknowledge continuing support by the Academic Fund Program at the National Research University Higher School of Economics(grant no 19-04-019 in 2018–2019) and by the International Decision Choice and Analysis Center(DECAN) NRU HSE, in the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the the Russian Academic Excellence Project“5–100”. S. N. acknowledges the support by FCT/MCTES, NOVA LINCS(UID/CEC/04516/2019).
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Mirkin, B., Frolov, D., Vlasov, A., Nascimento, S., Fenner, T. (2020). A Hybrid Approach to the Analysis of a Collection of Research Papers. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_40
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DOI: https://doi.org/10.1007/978-3-030-62365-4_40
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