Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 24, Issue 1, Pages 30-44Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2010.211
Keywords
Text mining; text classification; pattern mining; pattern evolving; information filtering
Categories
Funding
- Beijing Natural Science Foundation [4102007]
- Australian Research Council (ARC) [DP0988007]
- Australian Research Council [DP0988007] Funding Source: Australian Research Council
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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.
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