Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Modified PrefixSpan Method for Motif Discovery in Sequence Databases
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Semi-structure mining method for text mining with a chunk-based dependency structure
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Extracting trees of quantitative serial episodes
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
A flexible and efficient sequential pattern mining algorithm
International Journal of Intelligent Information and Database Systems
Finding frequent sub-trajectories with time constraints
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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Sequential pattern mining can be used to extract frequent sequences maintaining their transaction order. As conventional sequential pattern mining methods do not consider transaction occurrence time intervals, it is impossible to predict the time intervals of any two transactions extracted as frequent sequences. Thus, from extracted sequential patterns, although users are able to predict what events will occur, they are not able to predict when the events will occur. Here, we propose a new sequential pattern mining method that considers time intervals. Using Japanese earthquake data, we confirmed that our method is able to extract new types of frequent sequences that are not extracted by conventional sequential pattern mining methods.