Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
Relational Data Mining
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
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
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Top-down induction of first-order logical decision trees
Artificial Intelligence
First-order temporal pattern mining with regular expression constraints
Data & Knowledge Engineering
Multi-Dimensional Relational Sequence Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Tree pattern mining with tree automata constraints
Information Systems
Multi-Dimensional Relational Sequence Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Mining first-order temporal interval patterns with regular expression constraints
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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Sequence mining is an active research field of data mining because algorithms designed in that domain lead to various valuable applications. To increase efficiency of basic sequence mining algorithms, generally based on a levelwise approach, more recent algorithms try to introduce some constraints to prune the search space during the discovery process. Nevertheless, existing algorithms are actually limited to extract frequent sequences made up of items of a database. In this paper, we generalize the notion of sequence to define what we call logical sequence where each element of a sequence may contain some logical variables. Then we show how we can extend constrained sequence mining to constrained frequent logical sequence mining.