A New SQL-like Operator for Mining Association Rules.

@inproceedings{DBLP:conf/vldb/MeoPC96,
  author    = {Rosa Meo and
               Giuseppe Psaila and
               Stefano Ceri},
  editor    = {T. M. Vijayaraman and
               Alejandro P. Buchmann and
               C. Mohan and
               Nandlal L. Sarda},
  title     = {A New SQL-like Operator for Mining Association Rules},
  booktitle = {VLDB'96, Proceedings of 22th International Conference on Very
               Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India},
  publisher = {Morgan Kaufmann},
  year      = {1996},
  isbn      = {1-55860-382-4},
  pages     = {122-133},
  ee        = {db/conf/vldb/MeoPC96.html},
  crossref  = {DBLP:conf/vldb/96},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Data mining evolved as a collection of applicative problems and efficient solution algorithms relative to rather peculiar problems, all focused on the discovery of relevant information hidden in databases of huge dimensions. In particular, one of the most investigated topics is the discovery of association rules.

This work proposes a unifying model that enables a uniform description of the problem of discovering association rules. The model provides SQL-like operator, named {\em MINE RULE}, which is capable of expressing all the problems presented so far in the literature concerning the mining of association rules. We demonstrate the expressive power of the new operator by means of several examples, some of which are classical, while some others are fully original and correspond to novel and unusual applications. We also present the operational semantics of the operator by means of an extended relational algebra.

Copyright © 1996 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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Referenced by

1. Theodore Johnson, Laks V. S. Lakshmanan, Raymond T. Ng: The 3W Model and Algebra for Unified Data Mining. VLDB 2000: 21-32
2. Jean-François Boulicaut, Patrick Marcel, Christophe Rigotti: Query Driven Knowledge Discovery in Multidimensional Data. DOLAP 1999: 87-93
3. Marek Wojciechowski: Mining Various Patterns in Sequential Data in an SQL-like Manner. ADBIS (Short Papers) 1999: 131-138
4. Surajit Chaudhuri: Data Mining and Database Systems: Where is the Intersection? IEEE Data Eng. Bull. 21(1): 4-8(1998)
5. Sunita Sarawagi, Shiby Thomas, Rakesh Agrawal: Integrating Mining with Relational Database Systems: Alternatives and Implications. SIGMOD Conference 1998: 343-354
6. Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han, Alex Pang: Exploratory Mining and Pruning Optimizations of Constrained Association Rules. SIGMOD Conference 1998: 13-24
7. Rosa Meo, Giuseppe Psaila, Stefano Ceri: A Tightly-Coupled Architecture for Data Mining. ICDE 1998: 316-323
8. Tomasz Imielinski, Aashu Virmani: Association Rules... and What's Next? Towards Second Generation Data Mining Systems. ADBIS 1998: 6-25
9. Heikki Mannila: Methods and Problems in Data Mining. ICDT 1997: 41-55
10. Tadeusz Morzy, Maciej Zakrzewicz: SQL-Like Language for Database Mining. ADBIS 1997: 311-317
11. Tomasz Imielinski, Heikki Mannila: A Database Perspective on Knowledge Discovery. Commun. ACM 39(11): 58-64(1996)