Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Efficient mining of sequential patterns with time constraints: Reducing the combinations
Expert Systems with Applications: An International Journal
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
E3TP: A Novel Trajectory Prediction Algorithm in Moving Objects Databases
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Temporal mining for interactive workflow data analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the Temporal Dimension of the Information Propagation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
PutMode: prediction of uncertain trajectories in moving objects databases
Applied Intelligence
Extracting temporal patterns from interval-based sequences
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Constructing and comparing user mobility profiles for location-based services
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Protecting query privacy in location-based services
Geoinformatica
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In this paper we propose an extension of the sequence mining paradigm to (temporally-)annotated sequential patterns, where each transition in a sequential pattern is annotated with a typical transition time derived from the source data. Then, we present a basic solution for the novel mining problem based on the combination of sequential pattern mining and clustering, and assess this solution on two realistic datasets, illustrating how potentially useful patterns of the new form are extracted.