Abstract—Identification of meaningful patterns and trends in large volumes of text data is an important task in various fields. In the present study we crawled the keywords from the abstracts in IIE Transactions, one of the representative journals in the field of Industrial Engineering from 1969 to 2011. We applied a low-dimensional embedding method, clustering analysis, association rule, and social network analysis to find meaningful associative patterns of the keywords frequently appeared in the paper.
Index Terms—Data mining, text mining, clustering, association rule, social network analysis
The authors are with the School of Industrial Management Engineering, Korea University, Seoul, Korea (e-mail: sbkim1@korea.ac.kr.)
Cite: Su Gon Cho and Seoung Bum Kim, "Identification of Research Patterns and Trends through Text Mining," International Journal of Information and Education Technology vol. 2, no. 3, pp. 233-235, 2012.