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k-Means Clusterization and Machine Learning Prediction of European Most Cited Scientific Publications

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  • Leogrande, Angelo
  • Costantiello, Alberto
  • Laureti, Lucio

Abstract

In this article we investigate the determinants of the European “Most Cited Publications”. We use data from the European Innovation Scoreboard-EIS of the European Commission for the period 2010-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Results show that the level of “Most Cited Publications” is positively associated, among others, to “Innovation Index” and “Enterprise Birth” and negatively associated, among others, to “Government Procurement of Advanced Technology Products” and “Human Resources”. Furthermore, we perform a cluster analysis with the k-Means algorithm either with the Silhouette Coefficient and the Elbow Method. We find that the Elbow Method shows better results than the Silhouette Coefficient with a number of clusters equal to 3. In adjunct we perform a network analysis with the Manhattan distance, and we find the presence of 4 complex and 2 simplified network structures. Finally, we present a confrontation among 10 machine learning algorithms to predict the level of “Most Cited Publication” either with Original Data-OD either with Augmented Data-AD. Results show that the best machine learning algorithm to predict the level of “Most Cited Publication” with Original Data-OD is SGD, while Linear Regression is the best machine learning algorithm for the prediction of “Most Cited Publications” with Augmented Data-AD.

Suggested Citation

  • Leogrande, Angelo & Costantiello, Alberto & Laureti, Lucio, 2022. "k-Means Clusterization and Machine Learning Prediction of European Most Cited Scientific Publications," MPRA Paper 114273, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:114273
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    References listed on IDEAS

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    1. Leogrande, Angelo & Costantiello, Alberto & Laureti, Lucio, 2022. "The Export of Medium and High-Tech Products Manufactured in Europe," MPRA Paper 114215, University Library of Munich, Germany.
    2. Castillo-Vergara, Mauricio & Alvarez-Marin, Alejandro & Placencio-Hidalgo, Dario, 2018. "A bibliometric analysis of creativity in the field of business economics," Journal of Business Research, Elsevier, vol. 85(C), pages 1-9.
    3. Gaviria-Marin, Magaly & Merigó, José M. & Baier-Fuentes, Hugo, 2019. "Knowledge management: A global examination based on bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 194-220.
    4. Leogrande, Angelo & Costantiello, Alberto & Laureti, Lucio, 2022. "The Exports of Knowledge Intensive Services. A Complex Metric Approach," MPRA Paper 113348, University Library of Munich, Germany.
    5. Leogrande, Angelo, 2022. "Export of Medium and High-Tech Products in Europe," MPRA Paper 112619, University Library of Munich, Germany.
    6. Costantiello, Alberto & Laureti, Lucio & De Cristoforo, Gianluca & Leogrande, Angelo, 2021. "The Innovation-Sales Growth Nexus in Europe," MPRA Paper 106858, University Library of Munich, Germany, revised 28 Mar 2021.
    7. Leogrande, Angelo, 2022. "The Export of High Knowledge Intensity Services of European Countries," MPRA Paper 112795, University Library of Munich, Germany.
    8. Costantiello, Alberto & Laureti, Lucio & Leogrande, Angelo, 2022. "The Determinants of Lifelong Learning in Europe," MPRA Paper 114053, University Library of Munich, Germany.
    9. Leogrande, Angelo & Costantiello, Alberto, 2021. "Human Resources in Europe. Estimation, Clusterization, Machine Learning and Prediction," MPRA Paper 109749, University Library of Munich, Germany.
    10. Leogrande, Angelo & Costantiello, Alberto & Laureti, Lucio & Leogrande, Domenico, 2021. "The Determinants of Design Applications in Europe," MPRA Paper 110836, University Library of Munich, Germany.
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    Cited by:

    1. Costantiello, Alberto & Leogrande, Angelo, 2023. "The Impact of Research and Development Expenditures on ESG Model in the Global Economy," MPRA Paper 117013, University Library of Munich, Germany.

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    More about this item

    Keywords

    Innovation; and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation.;
    All these keywords.

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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