A rough set based approach for web user profiling

Abstract

E-governance plays a pivotal role in the domain of online services by ensuring round the clock accessibility of a wide spectrum of services. However, the huge amount of uploaded information and a vacillating user base makes it rather difficult to access the desired information from the portal. This requires a system which intelligently presents a personalized user interface. A challenging requirement in designing such a system is classifying the diversified users on the basis of their web experience. Traditional web usage mining techniques have been used to cluster similar users primarily on the basis of their page access patterns. In this paper, we veer our attention towards the level of user experience by introducing three parameters namely, page switching behavior, page probing behavior and session count which predominantly decide the level of experience acquired by e-governance users. We make an innovative use of Rough Set Theory to derive a rule-based classification system using three reduct optimization algorithms namely, Johnson Algorithm, Genetic Algorithm and Basic Minimal classification method. In order to test our system, we classified the user base that is publically available in the CTI dataset into two categories. The Basic Minimal method reports the highest accuracy of 74.90% with five fold cross validation.