International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 DOI : 10.5121/ijaia.2014.5403 35              Shampa Sengupta 1 , Asit Kumar Das 2 1 Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah – 711 204, West Bengal, India 2 Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah – 711 103, West Bengal, India ABSTRACT 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. KEYWORDS Dimension Reduction, Incremental Data, Dynamic Reduct, Rough Set Theory, PSO, classification System. 1. INTRODUCTION In today’s e-governance age, everything is being done through electronic media. So huge data is generated and collected from various areas for which proper data management is necessary. Retrieval of some interesting information from stored data as well as time variant data is also a very challenging task. Extraction of meaningful and useful pattern from these large data is the main objective of data mining technique [1, 2]. Data mining techniques basically uses the concept of database technology [3] and pattern recognition [5, 6] principles. Feature selection [7, 8] and reduct generation [9, 10] are frequently used as a pre-processing step to data mining and knowledge discovery. For static data, it selects an optimal subset of features from the feature space according to a certain evaluation criterion. In recent years, dimension of datasets are growing rapidly in many applications which bring great difficulty to data mining and pattern recognition. As datasets changes with time, it is very time consuming or even infeasible to run