GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
The SIFT information dissemination system
ACM Transactions on Database Systems (TODS)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM SIGKDD Explorations Newsletter
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Position coded pre-order linked WAP-tree for web log sequential pattern mining
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining Web navigation patterns with a path traversal graph
Expert Systems with Applications: An International Journal
An efficient web recommendation system based on modified IncSpan algorithm
International Journal of Knowledge and Web Intelligence
Extracting users' interest from book-browsing behaviors recorded with RFID
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
Extracting research communities from bibliographic data
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
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Sequential access pattern mining discovers interesting and frequent user access patterns from web logs. Most of the previous studies have adopted Apriori-like sequential pattern mining techniques, which faced the problem on requiring expensive multiple scans of databases. More recent algorithms that are based on the Web Access Pattern tree (or WAP-tree) can achieve an order of magnitude faster than traditional Apriori-like sequential pattern mining techniques. However, the use of conditional search strategies in WAP-tree based mining algorithms requires re-construction of large numbers of intermediate conditional WAP-trees during mining process, which is also very costly. In this paper, we propose an efficient sequential access pattern mining algorithm, known as CSB-mine (Conditional Sequence Base mining algorithm). The proposed CSB-mine algorithm is based directly on the conditional sequence bases of each frequent event which eliminates the need for constructing WAP-trees. This can improve the efficiency of the mining process significantly compared with WAP-tree based mining algorithms, especially when the support threshold becomes smaller and the size of database gets larger. In this paper, the proposed CSB-mine algorithm and its performance will be discussed. In addition, we will also discuss a sequential access-based web recommender system that has incorporated the CSB-mine algorithm for web recommendations.