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Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences (2002)
| Venue: | In Proceedings of the 13th International Symposium on Foundations of Intelligent Systems |
| Citations: | 31 - 3 self |
BibTeX
@INPROCEEDINGS{Harms02discoveringsequential,
author = {Sherri K. Harms and Jitender Deogun and Tsegaye Tadesse},
title = {Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences},
booktitle = {In Proceedings of the 13th International Symposium on Foundations of Intelligent Systems},
year = {2002},
pages = {432--441},
publisher = {Springer}
}
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Abstract
We present MOWCATL, an efficient method for mining frequent sequential association rules from multiple sequential data sets with a time lag between the occurrence of an antecedent sequence and the corresponding consequent sequence. This approach finds patterns in one or more sequences that precede the occurrence of patterns in other sequences, with respect to user-specified constraints. In addition to the traditional frequency and support constraints in sequential data mining, this approach uses separate antecedent and consequent inclusion constraints.
Keyphrases
time lag sequential association rule multiple sequence consequent inclusion constraint antecedent sequence frequent sequential association rule multiple sequential data set support constraint sequential data mining corresponding consequent sequence user-specified constraint efficient method present mowcatl traditional frequency separate antecedent
