Mining of Users’ Access Behaviour for Frequent Sequential Pattern from Web Logs

Sequential Pattern mining is the process of applying data mining techniques to a sequential database for the purposes of discovering the correlation relationships that exist among an ordered list of events. The task of discovering frequent sequences is challenging, because the algorithm needs to process a combinatorially explosive number of possible sequences. Discovering hidden information from Web log data is called Web usage mining. One common usage in web applications is the mining of users’ access behaviour for the purpose of predicting and hence pre-fetching the web pages that the user is likely to visit. The aim of discovering frequent Sequential patterns in Web log data is to obtain information about the access behaviour of the users. Finding Frequent Sequential Pattern (FSP) is an important problem in web usage mining. In this paper, we explore a new frequent sequence pattern technique called AWAPT (Adaptive Web Access Pattern Tree), for FSP mining. An AWAPT combines Suffix tree and Prefix tree for efficient storage of all the sequences that contain a given item. It eliminates recursive reconstruction of intermediate WAP tree during the mining by assigning the binary codes to each node in the WAP Tree. Web access pattern tree (WAP-tree) mining is a sequential pattern mining technique for web log access sequences, which first stores the original web access sequence database(WASD) on a prefix tree, similar to the frequent pattern tree (FP-tree) for storing non-sequential data. WAP-tree algorithm then, mines the frequent sequences from the WAP-tree by recursively re-constructing intermediate trees, starting with suffix sequences and ending with prefix sequences. An attempt has been made to AWAPT approach for improving efficiency. AWAPT totally eliminates the need to engage in numerous reconstructions of intermediate WAP-trees during mining and considerably reduces execution time.