Affiliations: School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand
Correspondence:
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Corresponding author: Thanaruk Theeramunkong, School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tivanond Road, Bangkadi, Muang, Pathumthani 12000, Thailand. Tel.: +66 (0) 2501 3505 20; E-mail: [email protected].
Abstract: Association rule mining can be applied to discover relations among news documents. Most existing approaches may not be good enough to extract meaningful news relations due to the limitation of having only single association measure for ranking mined news relations. This paper presents a region-based method to selectively use different association measures for different ranking regions, towards improvement of the ranking mechanism for news relation discovery. In this method, first the mined relations are sorted under a preliminary criterion to form a number of regions before scoring the relations in each region with different association measures. The meaningful news relations are discovered through three levels of relation: completely related, somehow related and unrelated relations, judged by the domain expert. To evaluate the proposed region-based ranking method, the method which has no region construction is considered to be the baseline by using a set of 1,132 news relations mined from 811 news documents. As performance criterion, a rank-order mismatch is explored to compare the qualitative results between the proposed method and the human evaluation. Compared to the baseline, the region-based method significantly improves performance by the average rank-order mismatch of 1.21%–28.32% for confidence and 4.83%–29.04% for conviction, respectively.
Keywords: Region-based ranking, news relation discovery, association rule mining, rank-order mismatch, text mining
