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
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining hybrid sequential patterns and sequential rules
Information Systems
Efficient Data Mining for Path Traversal Patterns
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
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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
Deriving two-stage learning sequences from knowledge in fuzzy sequential pattern mining
Information Sciences—Informatics and Computer Science: An International Journal
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
Mining fuzzy sequential patterns from quantitative transactions
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Incremental and interactive mining of web traversal patterns
Information Sciences: an International Journal
Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
Linguistic object-oriented web-usage mining
International Journal of Approximate Reasoning
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
A new approach for discovering fuzzy quantitative sequential patterns in sequence databases
Fuzzy Sets and Systems
Discovering fuzzy time-interval sequential patterns in sequence databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
From Crispness to Fuzziness: Three Algorithms for Soft Sequential Pattern Mining
IEEE Transactions on Fuzzy Systems
Discovering multi-label temporal patterns in sequence databases
Information Sciences: an International Journal
Information Sciences: an International Journal
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Mining sequential patterns to find ordered events or subsequence patterns is essential in many applications, such as analysis of consumer shopping data, web clickstreams, and biological sequences. Traditional patterns reveal which items are frequently purchased together and in what order. However, information about the time intervals between purchases is missing. Therefore, Yang proposed using multi-time-interval sequential patterns to consider the time intervals between each pair of items in a pattern. For example, means that Bread is bought before Milk within an interval of ti"1, and Jam is bought after Bread and Milk within intervals of ti"2 and ti"1, respectively, where ti"1 and ti"2 are predefined time intervals. Although this new type of pattern considers the intervals between all pairs of items, it contains a sharp boundary problem; that is, when the time interval between two purchases is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. In this study, we applied the concept of fuzzy sets to solve the sharp boundary problem. The discovered patterns, called fuzzy multi-time-interval sequential patterns, describe time intervals in linguistic terms for better understanding. Two algorithms, FuzzMI-Apriori and FuzzMI-PrefixSpan, were developed for mining fuzzy multi-time-interval patterns. Experiments using synthetic and real datasets showed the algorithms' computational efficiency, scalability, and effectiveness.