ASSOCIATION RULE MINING FOR WEB USAGE DATA

Abstract

The immense volume of web usage data that exists on web servers contains potentially valuable information about the behaviour of website visitors. This information can be used in various ways, such as enhancing the effectiveness of websites or developing directed web applications. Our focus on this dissertation is to applying association rules as a data mining technique to extract potentially useful knowledge from web usage data._ Association rule generation is a common problem in association rule mining that is further aggravated in web usage log mining due to the interconnectedness of web pages through the website link structure. We conducted a comprehensive analysis of web usage association rules found on a website of an educational institution. Here we proposed and applied a set of basic pruning schemes to reduce the rule set size and to remove a significant number of non-interesting rules. This pruning method decreased the size of our experimental rule set by more than three times, making it much simpler to browse for truly interesting rules. This can initiate a webmaster to action that can potentially enhance the website and improve its browsing experience.