Deploying Top-k Specific Patterns for Relevance Feature Discovery

The quality of discovered patterns is important for relevance feature discovery in text documents because frequent pattern mining often produces many noisy patterns. In this paper, we propose a novel method for the summarization of discovered patterns in text documents, finding a smaller number of specific patterns for representing the large number of discovered patterns for a given topic. We also evaluate the proposed method by implementing a new pattern-based information filtering model. The experimental results show that the proposed method not only outperforms both the term-based approaches and pattern based approaches, but largely reduces the number of feature terms as well.