Abstract— Relevance Feature Discovery for Text Mining. It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user < Final Year Projects 2016 > preferences because of large scale terms and data patterns. Most existing popular text mining and classiﬁcation methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery.