Web Mining Using Improved Apriori Algorithm

Abstract

In this study, we will be concentrating on one of the more recent advancements in data mining, specifically mining online usage. The purpose of web use mining is to gain usable knowledge from the data that web servers keep about the actions of its visitors by mining the data that is stored on such servers. By using the association rule generation in the Web domain, the pages that are most frequently referenced together can be combined into a single server session. This is possible because of the interconnected nature of the Web. In association rule mining, a technique known as frequent set mining is one of the methods that may be used to discover regular patterns from a web log file. When it comes to mining the usage of the web, the term association rules refers to groups of web pages that are accessed together and have a support value that is higher than a given threshold. The support can be expressed as a proportion of total transactions that match a particular pattern. With the aid of the presence or absence of association rules, web designers are able to effectively reconstruct the websites they have created for their clients. In this research, we have introduced a method called Aprior for the purpose of extracting frequent patterns from online log files. The findings of the experiments that were carried out on data relating to peoples use of the website indicate that general sequential patterns or frequent item sets are more suitable for use in Web customization and recommender systems.