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A clustering-based approach for the evaluation of candidate emerging technologies

Author

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  • Serkan Altuntas

    (Yildiz Technical University)

  • Zulfiye Erdogan

    (Iskenderun Technical University)

  • Turkay Dereli

    (Iskenderun Technical University
    Gaziantep University)

Abstract

The aim of this study is to propose a clustering-based approach based on patent information for the evaluation of candidate emerging technologies. The proposed approach uses patent analysis and clustering approaches in data mining. Patent analysis is a widely used method for the evaluation of candidate emerging technologies in the literature. The clustering algorithms used in this study are self-organizing maps, expected maximization and density-based clustering. A real-life application on dental implant technology is presented to show how the proposed approach works in practice. The contributions of this study are twofold. This study contributes to the literature by taking into account claims, forward citations, backward citations, technology cycle times, and technology scores for the evaluation of candidate emerging technologies. Second, the evaluation of dental implant technology with respect to claims, forward citations, backward citations, technology cycle times, and technology scores has not been conducted so far. The results obtained from the application shows that dental implant technology is an candidate emerging technology and the proposed approach can be easily conducted in real life case studies.

Suggested Citation

  • Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:2:d:10.1007_s11192-020-03535-0
    DOI: 10.1007/s11192-020-03535-0
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    References listed on IDEAS

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    3. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.

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