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On the Discovery of Interesting Patterns in Association Rules.

Sridhar Ramaswamy, Sameer Mahajan, Abraham Silberschatz: On the Discovery of Interesting Patterns in Association Rules. VLDB 1998: 368-379
@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},
  ee        = {db/conf/vldb/RamaswamyMS98.html},
  crossref  = {DBLP:conf/vldb/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

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.

Copyright © 1998 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.


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Ashish Gupta, Oded Shmueli, Jennifer Widom (Eds.): VLDB'98, Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA. Morgan Kaufmann 1998, ISBN 1-55860-566-5
Contents BibTeX

References

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Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216 BibTeX
[All85]
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[AS94]
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[ÍRS98]
Banu Özden, Sridhar Ramaswamy, Abraham Silberschatz: Cyclic Association Rules. ICDE 1998: 412-421 BibTeX
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...
[SA95]
Ramakrishnan Srikant, Rakesh Agrawal: Mining Generalized Association Rules. VLDB 1995: 407-419 BibTeX
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Ramakrishnan Srikant, Rakesh Agrawal: Mining Quantitative Association Rules in Large Relational Tables. SIGMOD Conference 1996: 1-12 BibTeX
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[Toi96]
Hannu Toivonen: Sampling Large Databases for Association Rules. VLDB 1996: 134-145 BibTeX

Referenced by

  1. Laks V. S. Lakshmanan, Raymond T. Ng, Jiawei Han, Alex Pang: Optimization of Constrained Frequent Set Queries with 2-variable Constraints. SIGMOD Conference 1999: 157-168
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