期刊
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
卷 27, 期 5, 页码 569-578出版社
KOREAN STATISTICAL SOC
DOI: 10.29220/CSAM.2020.27.5.569
关键词
mathematical subject classification; Markov chain clustering; entropy graph clustering
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2017R1A6A3A04005963, 2018R1D1 A1B07050910]
- Ministry of Science, ICT and Future Planning [2019R1A6A1A11051177]
- National Research Foundation of Korea [2017R1A6A3A04005963] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Various graph clustering methods have been introduced to identify communities in social or biological networks. This paper studies the entropy-based and the Markov chain-based methods in clustering the undirected graph. We examine the performance of two clustering methods with conventional methods based on quality measures of clustering. For the real applications, we collect the mathematical subject classification (MSC) codes of research papers from published mathematical databases and construct the weighted code-to-document matrix for applying graph clustering methods. We pursue to group MSC codes into the same cluster if the corresponding MSC codes appear in many papers simultaneously. We compare the MSC clustering results based on the several assessment measures and conclude that the Markov chain-based method is suitable for clustering the MSC codes.
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