Generate Personalized usage knowledge from the web access behavior using Clustering Techniques
International Journal of Scientific Engineering and Applied Science (IJSEAS)
The immense volume of web usage data that exists on web servers contains potentially valuable information about the behavior of website visitors. This information can be exploited in various ways, such as enhancing the effectiveness of websites or developing directed web marketing campaigns. In this paper we will focus on applying clustering algorithm as a data mining technique to extract potentially useful knowledge from web usage data. We conducted a comprehensive analysis of web usage mining
... of web usage mining techniques found on a website of an educational institution. Our experiments confirm that, prior to pruning, the set of generated clustering algorithm contained too many non-interesting rules, which made it very difficult for a user to find and exploit useful information. Many of these rules are a simple consequence of the high correlation between web pages due to their interconnectedness through the website link structure. We proposed and applied a set of basic clustering to reduce the rule set size and to remove a significant number of non-interesting data. The analysis of clustering algorithm in our case study confirmed the hypothesis that discovering interesting and potentially useful in web usage data that does not have to be a time consuming task and can lead to actions that increase the website's effectiveness.