Issue No. 01 - January (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.211
Ning Zhong , Maebashi Institute of Technology, Maebashi
Sheng-Tang Wu , Asia University, Taiwan
Yuefeng Li , Queensland University of Technology, Brisbane
Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.
Text mining, text classification, pattern mining, pattern evolving, information filtering.
Ning Zhong, Sheng-Tang Wu, Yuefeng Li, "Effective Pattern Discovery for Text Mining", IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 30-44, January 2012, doi:10.1109/TKDE.2010.211