This study explores technology clusters through patent analysis. The aim of exploring technology clusters is to grasp competitors' levels of sustainable research and development (R&D) and establish a sustainable strategy for entering an industry. To achieve this, we first grouped the patent documents with similar technologies by applying affinity propagation (AP) clustering, which is effective while grouping large amounts of data. Next, in order to define the technology clusters, we adopted the term frequency-inverse document frequency (TF-IDF) weight, which lists the terms in order of importance. We collected the patent data of Korean electric car companies from the United States Patent and Trademark Office (USPTO) to verify our proposed methodology. As a result, our proposed methodology presents more detailed information on the Korean electric car industry than previous studies.
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059742). This research was supported by the BK 21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University).
© 2016 by the author.
- Affinity propagation clustering
- Patent analysis
- Sustainable R&D
- TF-IDF weight
- Technology clusters
ASJC Scopus subject areas
- Geography, Planning and Development
- Renewable Energy, Sustainability and the Environment
- Environmental Science (miscellaneous)
- Energy Engineering and Power Technology
- Management, Monitoring, Policy and Law