Relevance Feature Discovery for Text Mining Using Feature Clustering

It is difficult to obtain the quality of relevance feature discovery in text mining because of large 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. However, pattern-based approaches yields better result than term-based approaches. So, we decided to implement a pattern based approach in our paper. This paper explains about the pattern-based approach in large text patterns. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). This paper uses Clustering technique to discover the relevant and irrelevant documents. It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.