J.Umarani, G.Thangraju, R.Arumugam
With the growth of World Wide Web and large number Hosts are join continuously to the internet, huge number of access events to Web sites pages were recorded by Servers in log files , many users share, send, post and download lot of things from Web Sites, this manner can be difficult to many organization and Agents in order to monitor and control that, the recorded information and type of analysis used to extract useful knowledge and understanding it become a practical challenges to many researchers. Log files can provide many events information regard to Clients activities, server activities and so on. Many organization employee many log files analysis tools to predict, analysis and monitor users behavior towards site contents .In this paper we proposed algorithms to analysis hidden information contents in Log files and discovering patterns by identified users along with them navigation behaviors then clustering similar users based on different interesting log file content for many Web sites that hosted in Web server. Find statistics for every part in log file command lines which are not present in many log files analysis tools are supported here and finally discovering frequent Web sites-Users and user's activities towards those Web sites.Forming a fine ordered web site to assist users to access the website effectively are the crisis in the modern information age. Web developer must recognize the structure of in viewer’s perception. Developer centered website is out of scope now a days. User centered websites have higher rating in the WWW. Navigation is important for novice users because most of the websites are dynamic in nature. Identifying accurate direction for target webpage is the essential commitment of each and every networked user. In this paper, we suggest a most favorable clarification for improved navigation for the users who accessing the dynamic website and using this they can find destination meticulously.Web Usage Mining (WUM) is applied to obtain information that may assist web site reorganization and adaptation. The classification algorithm, Longest Common Subsequence classifies user navigation.
Web Usage Mining, User Navigation, Adaptive Web site.