Paper Title
Generating Effective Patterns Having Relevance With Set Of Input Documents Given

Many years ago, there has been often held the hypothesis that pattern-based methods should perform better than term-based to describe user preferences; yet, how to use effectively large scale patterns remains a hard problem in text mining. To make a remedy in this challenging issue, this paper presents an innovative model for relevance feature discovery. It is a big challenge to guarantee the quality of discovered relevance features in text documents to describe user preferences because of data patterns and large scale terms. Most existing popular text mining and classification methods have adopted term-based approaches. It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. The objective of relevance feature discovery (RFD) is to find the useful features available in the text documents, including both relevant ones, for describing text mining results. Keywords— Text Mining, Text Feature Extraction, Text Classification.