Sequential Pattern Mining from Web Log Data

Sequential Pattern Mining involves applying data mining methods to large web data repositories to extract usage patterns. The growing popularity of the World Wide Web, many websites typically experience thousands of visitors every day. Analysis of who browsed what, can give important insight into the buying pattern of existing customers. Correct and timely decisions made based on this knowledge have helped organizations in reaching new heights in the market. In this paper, the sequence tree algorithm is implemented for pattern mining and this is experimented on web log data. The web log data which is considered as secondary data of the web has been considered for the discovery of frequent sequential patterns. The results have shown that the sequence tree algorithm performs better than the well-known Generalized Sequential Pattern (GSP) algorithm. The experiment shows that the running time of sequence tree algorithm is faster than the standard GSP algorithm and also Sequence Tree algorithm discovers more number of patterns than the standard GSP algorithm.