Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Language Support for Temporal Data Mining
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Framework for Temporal Data Mining
DEXA '98 Proceedings of the 9th International Conference on Database and Expert Systems Applications
A bibliography of temporal, spatial and spatio-temporal data mining research
ACM SIGKDD Explorations Newsletter
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th 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 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
Mining Calendar-Based Periodicities of Patterns in Temporal Data
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
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Mining patterns in a market-basket dataset is a well-stated problem. There are a number of approaches to deal with this problem. Different types of patterns may be present in a dataset. An interesting one is patterns that hold seasonally, which are called calendar-based patterns. Earlier methods require periods to be specified by the user. We present here a method which is able to extract different types of periodic patterns that may exist in a temporal market-basket dataset and it is not needed for the user to specify the periods in advance. We consider the time-stamps as a hierarchical data structure and then extract different types of patterns. The algorithm can detect both wholly and partially periodic patterns. Although we have applied our approach to market-basket dataset, the approach can be used for any event related dataset where the events are associated with time intervals.