Reduct Generation for the Incremental Data Using Rough Set Theory

In today’s changing world huge amount of data is generated and transferred frequently. Although the data is sometimes static but most commonly it is dynamic and transactional. New data that is being generated is getting constantly added to the old/existing data. To discover the knowledge from this incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time consuming. The paper proposes a dimension reduction algorithm that can be applied in dynamic environment for generation of reduced attribute set as dynamic reduct. The method analyzes the new dataset, when it becomes available, and modifies the reduct accordingly to fit the entire dataset. The concepts of discernibility relation, attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of dynamic reduct set, which not only reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed method has been applied on few benchmark dataset collected from the UCI repository and a dynamic reduct is computed. Experimental result shows the efficiency of the proposed method.