FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Parallel sequence mining on shared-memory machines
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Efficient mining of traversal patterns
Data & Knowledge Engineering - Building web warehouse
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining hybrid sequential patterns and sequential rules
Information Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns with Regular Expression Constraints
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
HierarchyScan: A Hierarchical Similarity Search Algorithm for Databases of Long Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Efficient Algorithms for Incremental Update of Frequent Sequences
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns
Information Processing and Management: an International Journal
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Pre-Processing Time Constraints for Efficiently Mining Generalized Sequential Patterns
TIME '04 Proceedings of the 11th International Symposium on Temporal Representation and Reasoning
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining Nonambiguous Temporal Patterns for Interval-Based Events
IEEE Transactions on Knowledge and Data Engineering
Data & Knowledge Engineering
Data & Knowledge Engineering
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
A change detection method for sequential patterns
Decision Support Systems
Generalization of pattern-growth methods for sequential pattern mining with gap constraints
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Analysis on repeat-buying patterns
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
On mining clinical pathway patterns from medical behaviors
Artificial Intelligence in Medicine
SART: a new association rule method for mining sequential patterns in time series of climate data
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Discovering metric temporal constraint networks on temporal databases
Artificial Intelligence in Medicine
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Sequential pattern mining is essential in many applications, including computational biology, consumer behavior analysis, web log analysis, etc. Although sequential patterns can tell us what items are frequently to be purchased together and in what order, they cannot provide information about the time span between items for decision support. Previous studies dealing with this problem either set time constraints to restrict the patterns discovered or define time-intervals between two successive items to provide time information. Accordingly, the first approach falls short in providing clear time-interval information while the second cannot discover time-interval information between two non-successive items in a sequential pattern. To provide more time-related knowledge, we define a new variant of time-interval sequential patterns, called multi-time-interval sequential patterns, which can reveal the time-intervals between all pairs of items in a pattern. Accordingly, we develop two efficient algorithms, called the MI-Apriori and MI-PrefixSpan algorithms, to solve this problem. The experimental results show that the MI-PrefixSpan algorithm is faster than the MI-Apriori algorithm, but the MI-Apriori algorithm has better scalability in long sequence data.