Improved Log Miner for Frequent Items Generation

Abstract Web log mining is the newest technology of data mining. There are various web related activities that are taken into consideration. Such data are mostly structured in nature as they are collected from various web pages and other web logs that are maintained in the server. Web Mining is divided into three types web content mining, web usage mining and web structure mining. In case of Web usage minings, the main aim and area is to focus on Web users and to learn the way they interacts with various Web sites available. As web log data are mostly noisy and extremely ambiguous, still there is a way where we can discover useful information and structure in the way the users interacts with a web site. The main objective of using mining is to quickly and automatically identify users from the vast log data. We can identify information such as frequent access paths, frequent access page groups and then cluster the users. With the help of web usage mining algorithms, the web application server logs, registration information, the user interest and other data such as user access patterns can be mined which will be helpful in laying foundation for decision making of organizations