Name
Abstract | ||
|---|---|---|
Web Usage Mining is the application of data mining techniques to large Web log databases in order to extract usage patterns. A cross-transaction association rule describes the association relationships among different user transactions in Web logs. In this paper, a Linear time intra-transaction frequent itemsets mining algorithm and the closure property of frequent itemsets are used to mining cross-transaction association rules from web log databases. We give the related preliminaries and present an efficient algorithm for efficient mining frequent cross-transaction closed pageviews sets in large Web log database. An extensive performance study shows that our algorithm can mining cross-transaction web usage patterns from large database efficiently. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11534310_56 | ICCNMC |
Keywords | Field | DocType |
large database,web usage mining,efficient mining,web log,large web log database,mining cross-transaction web usage,cross-transaction web usage pattern,frequent cross-transaction,mining cross-transaction association rule,cross-transaction association rule,frequent itemsets mining algorithm,data mining technique,data mining,linear time,association rule | Transaction processing,Mobile computing,Data mining,Web mining,Computer science,Association rule learning,Information extraction,Page view,Database transaction,Time complexity,Database | Conference |
Volume | ISSN | ISBN |
3619 | 0302-9743 | 3-540-28102-9 |
Citations | PageRank | References |
3 | 0.39 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Chen | 1 | 95 | 23.32 |
| Liangyi Ou | 2 | 3 | 0.39 |
| Jian Yin | 3 | 105 | 6.71 |
| Jin Huang | 4 | 70 | 6.22 |