Mining Access Patterns Efficiently from Web Logs

By Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Hua Zhu. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2000

634 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Frequent pattern mining: current status and future directions (2007, methodology): “Web access patterns via association rule mining in web logs were proposed by (Chen et al. 1996; Pei et al. 2000; Srivastava et al. 2000; Punin and Krishnamoorthy 2001).”
A taxonomy of sequential pattern mining algorithms (2010, methodology): “2001] with projected databases; WAP-Mine [Pei et al. 2000] with WAP-tree; and PLWAP [Lu and Ezeife 2003] with its PLWAP-tree.”
Web Usage Mining as a Tool for Personalization: A Survey (2003, methodology): “Time-based methods have been used in most of the literature, (e.g. Catledge and Pitkow, 1995; Borges and Levene, 1999; Nasraoui et al., 1999; Wu et al., 1998; Paliouras et al., 2000a and Pei et al., 2000).”
Data Mining for Web Personalization (2007, background): “It is also possible to insert frequent sequences (after or during sequential pattern mining) into a trie structure [104, 86].”
Mining interesting knowledge from weblogs: a survey (2005, methodology): “A tree structure named WAP-tree is also introduced in [36] to register access sequence to Web pages, this structure is optimized to exploit the sequence mining algorithm developed by the same authors [36].”