User behavior analysis in web log through comparative study of Eclat and Apriori

As we know World Wide Web plays vital role in serving the needs of the user's on web. The web log files are generated as a result of an interaction between the client and the service provider on web. Web log file contains the massive hidden valuable information pertaining to the visitors, if mined can be used for predicting the navigation behavior of the users. However the task of discovering frequent sequence patterns from the web log is challenging. Sequential pattern mining provides a significant role in serving a promising approach of the access behavior of the user. This paper focuses on adopting an intelligent technique that can provide personalized web service for accessing related web pages more efficiently and effectively, so that it can be determined which web pages are more likely to be accessed by the user in future. This paper uses two intelligent algorithms for predicting the user behavior's namely Apriori and Eclat and also does the performance comparison of the two algorithms in terms of time and space complexity for the filtered data.