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On the Discovery of Interesting Patterns in Association Rules |
Full Paper (PDF)
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Many decision support systems, which utilize association rules for discovering interesting patterns, require the discovery of association rules thatvary over time.
Such rules describe complicated temporal patterns such as events that occur on the "first working day of every month."
In this paper, we study the problem of discovering how association rules vary over time.
In particular, we introduce the idea of using a calendar algebra todescribe complicated temporal phenomena of interest to the user.
We then present algorithms for discovering calendric association rules, which are association rules that follow the patterns set forth in theuser supplied calendar expressions.
We devise various optimizations that speed up the discovery of calendric association rules.
We show, through an extensive series of experiments, that these optimization techniques provide performance benefits ranging from 5% to 250% over a less sophisticated algorithm.
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References, where available, link to the DBLP on the World Wide Web.
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@inproceedings{DBLP:conf/vldb/RamaswamyMS98,
author = {Sridhar Ramaswamy and
Sameer Mahajan and
Abraham Silberschatz},
editor = {Ashish Gupta and
Oded Shmueli and
Jennifer Widom},
title = {On the Discovery of Interesting Patterns in Association Rules},
booktitle = {VLDB'98, Proceedings of 24rd International Conference on Very
Large Data Bases, August 24-27, 1998, New York City, New York,
USA},
publisher = {Morgan Kaufmann},
year = {1998},
isbn = {1-55860-566-5},
pages = {368-379},
crossref = {DBLP:conf/vldb/98},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
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