Discovering patterns in sequences of events
Artificial Intelligence
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Multi-dimensional sequential pattern mining
Proceedings of the tenth international conference on Information and knowledge management
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
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
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
M2SP: mining sequential patterns among several dimensions
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
An entropy-based approach for generating multi-dimensional sequential patterns
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Up and Down: Mining Multidimensional Sequential Patterns Using Hierarchies
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Mining Sequential Patterns with Negative Conclusions
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Mining multidimensional and multilevel sequential patterns
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining convergent and divergent sequences in multidimensional data
International Journal of Business Intelligence and Data Mining
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Mining data warehouses is still an open problem as few approaches really take the specificities of this framework into account (e.g. multidimensionality, hierarchies, historized data). Multidimensional sequential patterns have been studied but they do not provide any way to handle hierarchies. In this paper, we propose an original sequential pattern extraction method that takes the hierarchies into account. This method extracts more accurate knowledge and extends our preceding M2SP approach. We define the concepts related to our problems as well as the associated algorithms. The results of our experiments confirm the relevance of our proposal.