Discovering patterns in sequences of events
Artificial Intelligence
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
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
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
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
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th 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 All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference 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
An Efficient Algorithm for Mining Frequent Sequences by a New Strategy without Support Counting
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
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
Building the Data Warehouse
On condensed representations of constrained frequent patterns
Knowledge and Information Systems
HYPE: mining hierarchical sequential patterns
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
PAID: Mining Sequential Patterns by Passed Item Deduction in Large Databases
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Algorithms for Context Based Sequential Pattern Mining
Fundamenta Informaticae
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
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Towards an automatic construction of Contextual Attribute-Value Taxonomies
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Healthcare trajectory mining by combining multidimensional component and itemsets
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Mining high coherent association rules with consideration of support measure
Expert Systems with Applications: An International Journal
Semi-supervised learning on closed set lattices
Intelligent Data Analysis
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Multidimensional databases have been designed to provide decision makers with the necessary tools to help them understand their data. This framework is different from transactional data as the datasets contain huge volumes of historicized and aggregated data defined over a set of dimensions that can be arranged through multiple levels of granularities. Many tools have been proposed to query the data and navigate through the levels of granularity. However, automatic tools are still missing to mine this type of data in order to discover regular specific patterns. In this article, we present a method for mining sequential patterns from multidimensional databases, at the same time taking advantage of the different dimensions and levels of granularity, which is original compared to existing work. The necessary definitions and algorithms are extended from regular sequential patterns to this particular case. Experiments are reported, showing the significance of this approach.