A on-line news documents clustering method

Authors:
Hui Zhang;Guo-hui Li;Xin-wen Xu
Affiliations:
Department of Engineering, School of Information System and Management, National University of Defense Technology, Changsha, China;Department of Engineering, School of Information System and Management, National University of Defense Technology, Changsha, China;College of Basic Education for officers, National University of Defense Technology, Changsha, China
Venue:
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Year:
2012

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Abstract

To improve the efficiency and accuracy of on-line news event detection (ONED) method, we select the words that their term frequency (TF) is greater than a threshold to create the vector space model of the news document, and propose a two-stage clustering method for ONED. This method divides the detection process into two stages. In the first stage, the similar documents collected in a certain period of time are clustered into micro-clusters. In the second stage, the micro-clusters are compared with previous event clusters. The experimental results show that the proposed method has fewer computation load, higher computing rate, and less loss of accuracy.