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
Discovery of Temporal Patterns. Learning Rules about the Qualitative Behaviour of Time Series
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
From Shell Logs to Shell Scripts
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Actions and Events in Interval Temporal Logic
Actions and Events in Interval Temporal Logic
Mining frequent logical sequences with SPIRIT-LoG
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
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
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Most methods for temporal pattern mining assume that time is represented by points in a straight line starting at some initial instant. In this paper, we consider a new kind of first order temporal pattern, specified in Allen's Temporal Interval Logic, where time is explicitly represented by intervals. We present the algorithm MILPRIT for mining temporal interval patterns, which uses variants of the classical level-wise search algorithms. MILPRIT allows a broad spectrum of constraints over temporal patterns to be incorporated in the mining process. Some experimental results over synthetic and real data are presented.