Study On Application Of Text Mining Based On Rough Set Theory

Data mining and text mining is to examine how access to knowledge of the important areas. Rough set theory is a classification capability while maintaining the same premise, through the concept of attribute reduction classification rules derived a new soft computing method, the theory in the application of text mining research has important theoretical significance and practical value.In this paper, the rough set theory in the application of methods of text mining studies, including those based on clear matrix attribute reduction algorithm Apriori-based association rule mining two aspects.First, In this paper, the first reduction of the decision-making table, delete the redundant entries, and then generate the distinct matrix; in Clear matrix of the attribute reduction process, through the extraction of nuclear properties and logical operations simplification, reducing the generation of intermediate redundant paradigm to improve the efficiency of the algorithm. Second, Based on the nature of the Apriori algorithm , while scaning the database at the same time generate the candidate set to delete the item does not meet the nature, Reduce the size of the database, thereby reducing the time consumed by scanning the database.Compared with the traditional classical algorithm, the improved algorithm in run-time save a lot of time and space consumption, in dealing with large-scale text database mining performance has improved so much.