Web log mining with adaptive support thresholds

Jian Chih Ou, Chang-Hung Lee, Ming-Syan Chen

Abstract: With the fast increase in Web activities, Web data mining has recently become an important research topic. However, most previous studies of mining path traversal patterns are based on the model of a uniform support threshold without taking into consideration such important factors as the length of a pattern, the positions of Web pages, and the importance of a particular pattern, etc. In view of this, we study and apply the Markov chain model to provide the determination of support threshold of Web documents. Furthermore, by properly employing some techniques devised for joining reference sequences, a new mining procedure of Web traversal patterns is proposed in this paper.