Extracting Frequent Sequences from Web Log Data using Sequence Tree Algorithm

Abstract: Sequential Pattern mining is the process of applying data mining techniques to large web data repositories.With the extensive use of Internet, discovery and analysis of useful information from the World Wide Web becomes a practical necessity. Data mining techniques are applied to a sequential database to discover the correlation relationships that exists among the ordered list of events. In this kind of mining, hidden data is extracted to get useful information which helps in knowing the browsing patterns of the users. Web usage mining is a data mining method that can be used in recommending the web usage patterns with the help of users� session and behaviour. The aim of discovering frequent sequential patterns in Web log data is to obtain information about the access behaviour of the users. It helps to understand the buying pattern of the existing customers. This paper focuses on the performance of the sequence tree algorithm which is better than the Generalized Sequential Pattern (GSP) algorithm. This paper emphasizes on the running time of sequence tree algorithm and its ability to discover more number of patterns than the standard GSP algorithm.

Keywords: Sequential Pattern Mining, Web usage mining, Generalized Sequential Pattern (GSP), Sequence tree algorithm.