Mining asynchronous periodic patterns in time series data
Proceedings of the sixth 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
Infominer: mining surprising periodic patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding event-oriented patterns in long temporal sequences
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining fuzzy periodic association rules
Data & Knowledge Engineering
Effective database transformation and efficient support computation for mining sequential patterns
Journal of Intelligent Information Systems
On probabilistic models for uncertain sequential pattern mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Mining sequential patterns from probabilistic databases
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
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Pattern discovery in temporal event sequences is of greatimportance in many application domains, such as telecommunicationnetwork fault analysis. In reality, not every typeof event has an accurate timestamp. Some of them, definedas inaccurate events in this paper, may only have an intervalas possible time of occurrence. The existence of inaccurateevents may cause uncertainty in event ordering. Thetraditional support model cannot deal with this uncertainty,which would cause some interesting patterns to be missing.In this paper, a new concept, precise support, is introducedto evaluate the probability of a pattern contained in a sequence.Based on this new metric, we define the uncertaintymodel and present an algorithm to discover interesting patternsin the sequence database that has one type of inaccurateevent. In our model, the number of types of inaccurateevents can be extended to k readily, however, at a cost ofincreasing computational complexity.