Research history generation using maximum margin clustering of research papers based on metainformation

Authors:
Manh Cuong Nguyen;Daichi Kato;Haruo Yokota;Taiichi Hashimoto
Affiliations:
Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan;Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan;Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan;Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan
Venue:
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Year:
2011

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Abstract

Our research aim is the automatic generation of a researcher's research history from research articles published on the internet. Research history generation based on the k-Means clustering algorithm has been proposed in previous work. However, the performance of the k-Means algorithm is unsatisfactory. We propose a method based on Maximum Margin Clustering (MMC). MMC is a new clustering algorithm based on Support Vector Machines (SVM). It is known that MMC is better than existing clustering algorithms such as k-Means. In this paper, we describe how to convert articles into vectors using metainformation about them and how to decide an initial setting for MMC automatically. We demonstrate by experiment that the purity of a method based on MMC is about 0.58 and its entropy is about 0.415. This result is better than that achieved in previous work (purity: 0.35, entropy: 0.47).