Mining Association Rules from Web Logs by incorporating Structural Knowledge of Website

One of the basic problems with the Association Rule discovery is that when Mining Algorithms are applied on Web Access Logs, the total number of generated rules is found to be very large. For finding useful results from these rules, the analyzer needs to look into large rule-set. Moreover, the analysis of such rule-set also requires certain criteria for making decisions, i.e. a particular rule should be accepted or not. This ambiguity of acceptance or rejection of rules makes it very difficult to extract knowledge. Hence in order to get effective results with the minimized effort, number of rules should be less and valid. Therefore, the structural knowledge of Website is considered to solve the purpose, that plays an important role in pruning the invalid rules, thereby reducing the size of rule-set , and it is observed from the experiment that the number of rules have been successfully reduced.