Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Maintaining knowledge about temporal intervals
Communications of the ACM
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Efficient mining of traversal patterns
Data & Knowledge Engineering - Building web warehouse
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
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
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Discovering Temporal Patterns for Interval-Based Events
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Efficient Constraint-Based Sequential Pattern Mining Using Dataset Filtering Techniques
Proceedings of the Baltic Conference, BalticDB&IS 2002 - Volume 1
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining sequential patterns from data streams: a centroid approach
Journal of Intelligent Information Systems
Mining Nonambiguous Temporal Patterns for Interval-Based Events
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
Mining typical patterns from databases
Information Sciences: an International Journal
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Data & Knowledge Engineering
An approach to discovering multi-temporal patterns and its application to financial databases
Information Sciences: an International Journal
Knowledge gathering of fuzzy multi-time-interval sequential patterns
Information Sciences: an International Journal
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
Dynamic time warping constraint learning for large margin nearest neighbor classification
Information Sciences: an International Journal
TempoXML: Nested bitemporal relationship modeling and conversion tool for fuzzy XML
Information Sciences: an International Journal
Efficient bitmap-based indexing of time-based interval sequences
Information Sciences: an International Journal
A modified parallel optimization system for updating large-size time-evolving flow matrix
Information Sciences: an International Journal
Generalized association rule mining with constraints
Information Sciences: an International Journal
On mining clinical pathway patterns from medical behaviors
Artificial Intelligence in Medicine
Multi-label ensemble based on variable pairwise constraint projection
Information Sciences: an International Journal
Information Sciences: an International Journal
Discovering metric temporal constraint networks on temporal databases
Artificial Intelligence in Medicine
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Sequential pattern mining is one of the most important data mining techniques. Previous research on mining sequential patterns discovered patterns from point-based event data, interval-based event data, and hybrid event data. In many real life applications, however, an event may involve many statuses; it might not occur only at one certain point in time or over a period of time. In this work, we propose a generalized representation of temporal events. We treat events as multi-label events with many statuses, and introduce an algorithm called MLTPM to discover multi-label temporal patterns from temporal databases. The experimental results show that the efficiency and scalability of the MLTPM algorithm are satisfactory. We also discuss interesting multi-label temporal patterns discovered when MLTPM was applied to historical Nasdaq data.