Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining contiguous sequential patterns from web logs
Proceedings of the 16th international conference on World Wide Web
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
Web Search Results Clustering Based on a Novel Suffix Tree Structure
ATC '08 Proceedings of the 5th international conference on Autonomic and Trusted Computing
Algorithm of mining sequential patterns for web personalization services
ACM SIGMIS Database
Clustering event logs using iterative partitioning
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Generalization of web log datas using WUM technique
ICNVS'10 Proceedings of the 12th international conference on Networking, VLSI and signal processing
A taxonomy of sequential pattern mining algorithms
ACM Computing Surveys (CSUR)
Co-clustering analysis of weblogs using bipartite spectral projection approach
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
A Method for Privacy Preserving Mining of Association Rules Based on Web Usage Mining
WISM '10 Proceedings of the 2010 International Conference on Web Information Systems and Mining - Volume 01
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Discovering access patterns from web log data is a typical sequential pattern mining application, and a lot of access pattern mining algorithms have been proposed. In this paper, we propose an improved approach of Gap-BIDE algorithm to extract user access patterns from web log data. Compared with the previous Gap-BIDE algorithm, a process of getting a large event set is proposed in the provided algorithm; the proposed approach can find out the frequent events by discarding the infrequent events which do not occur continuously in an accessing time before generating candidate patterns. In the experiment, we compare the previous access pattern mining algorithm with the proposed one, which shows that our approach is very efficient in discovering access patterns in large database.