Web log mining with adaptive support thresholds

Authors:
Jian-Chih Ou;Chang-Hung Lee;Ming-Syan Chen
Affiliations:
National Taiwan University, Taipei, Taiwan, ROC;BenQ Corporation, Taipei, Taiwan, ROC;National Taiwan University, Taipei, Taiwan, ROC
Venue:
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Year:
2005

Citing 3
Cited 0

The anatomy of a large-scale hypertextual Web search engine

WWW7 Proceedings of the seventh international conference on World Wide Web 7
Mining association rules with multiple minimum supports

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Data Mining for Path Traversal Patterns

IEEE Transactions on Knowledge and Data Engineering

Quantified Score

Hi-index	0.00

Visualization

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.