Abstract
With the growth of knowledge sharing, an increasingly large amount of Open-Access academic resources are being stored online. This paper systematically studies the method of mining knowledge communication via Open-Access Journals. We first designed a new framework of knowledge communication analysis based on clustering and association rule mining. Then, we proposed two improved indexes named cited frequency and weighted cited frequency. Extensive evaluations using real-world data validate the effectiveness of the proposed framework of knowledge communication analysis.
The research work described in this article has been supported by a project granted from the National Social Science Foundation of China (Project No. 14CTQ041), and a grant from MOE (Ministry of Education of China) Project of Humanity and Social Science Youth Fund (Project No. 12YJC870012).
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Wu, Q., Wu, Q., Zhao, S., Wei, M., Wang, F.L. (2015). Knowledge Communication Analysis Based on Clustering and Association Rules Mining. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_6
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DOI: https://doi.org/10.1007/978-3-319-22324-7_6
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