A Study on Rough Set Theory Based Dynamic Reduct for Classification System Optimization

In the present day huge amount of data is generated in every minute 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. Again to analyze the datasets properly, construction of efficient classifier model is necessary. The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The paper proposes a dimension reduction algorithm that can be applied in dynamic environment for generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the reduct and build up the corresponding classification system. The method analyzes the new dataset, when it becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire data set, interesting optimal classification rule sets are generated. The concepts of discernibility relation, attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of dynamic reduct set, and optimal classification rules are selected using PSO method, which not only reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed method has been applied on some benchmark dataset collected from the UCI repository and dynamic reduct is computed, and from the reduct optimal classification rules are also generated. Experimental result shows the efficiency of the proposed method.