Analysis of visitor's behavior from web log using web log expert tool

Abstract

Web usage mining is a data mining technique. There are large amount of data are stored on the internet. When user search any particular information by search engine like Google, Bing etc. is very difficult because the complexity of web pages is increases day by day. Web usage mining plays an important role to solve this problem. In web usage mining we are creating a suitable pattern according to the user's visiting behavior. The goal of this paper is to implement a web log Expert tool on web server log file (an educational institution web log data) to find the behavioral pattern and profiles of users interacting with a web site. The web mining usage pattern of an Technical Institution web data. Web related data is coteries in to three parts namely web log, access log, error log and proxy log data and collect the data in web server and implemented a web log expert. Our experimental results help to predict and identify the number of visitor for the website and improve the website usability. The web related log data are three types, namely proxy log data, web log data, and error log data. We exploration the activity statistic by daily based hourly based week and monthly based report of web usage pattern. The web usage mining is playing an important role to improve the availability of information of your web site.