A Comparative Study of Rule Mining Based Web Usage Mining Algorithms

Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the web. It also becomes very critical for effective web site management and for creating adaptive web sites, business and support services etc. The web mining field encompasses a wide array of issues, primarily aimed at deriving actionable knowledge from the web, and includes researchers from information retrieval, database technologies, and artificial intelligence. Most data used for mining is collected from web servers, clients, proxy servers, or server databases, all of which generate noisy data. Because web mining is sensitive to noise, data cleaning methods are necessary. Web usage mining itself can be classified further depending on the kind of usage data considered. They are web server data, application server data and application level data. Web server data correspond to the user logs that are collected at web server. This work compares the two standard web usage mining algorithms namely Apriori algorithm and Frequent Pattern algorithm. Particularly, this work focused on discovering the web usage patterns of websites from the server log files.