An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Mining asynchronous periodic patterns in time series data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
Mining periodic patterns with gap requirement from sequences
ACM Transactions on Knowledge Discovery from Data (TKDD)
Finding calendar-based periodic patterns
Pattern Recognition Letters
Finding locally and periodically frequent sets and periodic association rules
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
A parallel algorithm for mining multiple partial periodic patterns
Information Sciences: an International Journal
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An efficient algorithm with a worst-case time complexity of O(n logn) is proposed for detecting seasonal (calendar-based) periodicities of patterns in temporal datasets. Hierarchical data structures are used for representing the timestamps associated with the data. This representation facilitates the detection of different types of seasonal periodicities viz. yearly periodicities, monthly periodicities, daily periodicities etc. of patterns in the temporal dataset. The algorithm is tested with real-life data and the results are given.