Fast discovery of association rules
Advances in knowledge discovery and data mining
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
An Region-Based Learning Approach to Discovering Temporal Structures in Data
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Modelling Discrete Event Sequences as State Transition Diagrams
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
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Finding hidden temporal structures from event sequences is a difficult task, particularly when events occur irregularly over time and temporal dependencies may exist in a long time horizon. The tasks involved are not only to find event patterns represented in the form of temporal orders, but more importantly to find patterns that are described with precise time conditions and rules that can be applied to predict when a future event will occur. Recent study has shown that a new approach based on learning temporal regions is a good solution for this problem. This paper investigates this approach in a greater depth and makes several improvements. It introduces multiple rule selection methods to better uncover hidden relations. It also introduces heuristic rule pruning methods to speed up search to solve large-scale problems. Experimental results are presented which show the effectiveness of the new methods.