Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Towards on-line analytical mining in large databases
ACM SIGMOD Record
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
i3: intelligent, interactive investigation of OLAP data cubes
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
Understanding facts in a multidimensional object-oriented model
Proceedings of the 4th ACM international workshop on Data warehousing and OLAP
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
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
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th 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
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Ad-Hoc Association-Rule Mining within the Data Warehouse
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 8 - Volume 8
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
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Distributed and Parallel Databases
Enhanced mining of association rules from data cubes
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
HYPE: mining hierarchical sequential patterns
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Mining approximate top-k subspace anomalies in multi-dimensional time-series data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Web usage mining: extracting unexpected periods from web logs
Data Mining and Knowledge Discovery
Mining conjunctive sequential patterns
Data Mining and Knowledge Discovery
M2SP: mining sequential patterns among several dimensions
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Mining context based sequential patterns
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Assisting scientific discovery with an adaptive problem solver
DS'05 Proceedings of the 8th international conference on Discovery Science
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
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Online Analytical Processing (OLAP) mining provides useful knowledge to decision makers from multidimensional data stored in data warehouses. However, it is still difficult to find data mining tools taking all the data specificities (e.g., multidimensionality, hierarchies, time) into account. In this paper, we propose an original method to discover multidimensional sequential patterns among several levels of hierarchies. We define two types of multidimensional sequences: convergent sequences, where the elements become more and more precise, and divergent sequences where the elements become more and more general. A pattern-growth based algorithm is proposed and is shown to be efficient in our experiments.