Discovering Semantic Association Rules using Apriori and kth Markov Model on Social Mining

The Semantic Web opens up new opportunities for the data mining research. Identification of the current interests of the user based on the short-term navigational patterns instead of explicit user information has proved to be one of the potential sources for prediction of pages which may be of interest to the user. This would help organizations in various analyses such as web site improvement. Various techniques are employed for achieving personalized recommendation. In this research employs web usage mining techniques for determining the interest of "similar" users, technique for classifying and matching an online user based on his browsing interests. A novel approach for prediction of unvisited pages has been employed. The complete process for next page prediction, represented in the architecture broadly consists of two components: offline component and online component.