Discovering patterns in sequences of events
Artificial Intelligence
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Multi-dimensional sequential pattern mining
Proceedings of the tenth international conference on Information and knowledge 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
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
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
Mining unexpected multidimensional rules
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Up and Down: Mining Multidimensional Sequential Patterns Using Hierarchies
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Mining Multidimensional Sequential Patterns over Data Streams
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 sequential patterns aims at discovering correlations between events through time. However, even if many works have dealt with sequential pattern mining, none of them considers frequent sequential patterns involving several dimensions in the general case. In this paper, we propose a novel approach, called M2SP, to mine multidimensional sequential patterns. The main originality of our proposition is that we obtain not only intra-pattern sequences but also inter-pattern sequences. Moreover, we consider generalized multidimensional sequential patterns, called jokerized patterns, in which some of the dimension values may not be instanciated. Experiments on synthetic data are reported and show the scalability of our approach.