Query Flocks: A Generalization of Association-Rule Mining.

Shalom Tsur, Jeffrey D. Ullman, Serge Abiteboul, Chris Clifton, Rajeev Motwani, Svetlozar Nestorov, Arnon Rosenthal: Query Flocks: A Generalization of Association-Rule Mining. SIGMOD Conference 1998: 1-12

@inproceedings{DBLP:conf/sigmod/TsurUACMNR98,
  author    = {Shalom Tsur and
               Jeffrey D. Ullman and
               Serge Abiteboul and
               Chris Clifton and
               Rajeev Motwani and
               Svetlozar Nestorov and
               Arnon Rosenthal},
  editor    = {Laura M. Haas and
               Ashutosh Tiwary},
  title     = {Query Flocks: A Generalization of Association-Rule Mining},
  booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
               on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-995-5},
  pages     = {1-12},
  ee        = {http://doi.acm.org/10.1145/276304.276306, db/conf/sigmod/TsurUACMNR98.html},
  crossref  = {DBLP:conf/sigmod/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

BibTeX

Abstract

Association-rule mining has proved a highly successful technique for extracting useful information from very large databases. This success is attributed not only to the appropriateness of the objectives, but to the fact that a number of new query-optimization ideas, such as the ``a-priori'' trick, make association-rule mining run much faster than might be expected. In this paper we see that the same tricks can be extended to a much more general context, allowing efficient mining of very large databases for many different kinds of patterns. The general idea, called ``query flocks,'' is a generate-and-test model for data-mining problems. We show how the idea can be used either in a general-purpose mining system or in a next generation of conventional query optimizers.

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Laura M. Haas, Ashutosh Tiwary (Eds.): SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA. ACM Press 1998, ISBN 0-89791-995-5 BibTeX , SIGMOD Record 27(2), June 1998
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References

[AHV95]: Serge Abiteboul, Richard Hull, Victor Vianu: Foundations of Databases. Addison-Wesley 1995, ISBN 0-201-53771-0
Contents BibTeX
[AIS93]: Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216 BibTeX
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[Man97]: Heikki Mannila: Methods and Problems in Data Mining. ICDT 1997: 41-55 BibTeX
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Contents BibTeX
[Ull89]: Jeffrey D. Ullman: Principles of Database and Knowledge-Base Systems, Volume II. Computer Science Press 1989, ISBN 0-7167-8162-X
Contents BibTeX
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BibTeX
[ZO93]: Xubo Zhang, Z. Meral Özsoyoglu: On Efficient Reasoning with Implication Constraints. DOOD 1993: 236-252 BibTeX

Referenced by

Themistoklis Palpanas: Knowledge Discovery in Data Warehouses. SIGMOD Record 29(3): 88-100(2000)
Theodore Johnson, Laks V. S. Lakshmanan, Raymond T. Ng: The 3W Model and Algebra for Unified Data Mining. VLDB 2000: 21-32
Shinichi Morishita, Jun Sese: Traversing Itemset Lattice with Statistical Metric Pruning. PODS 2000: 226-236
Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew Tomkins: Extracting Large-Scale Knowledge Bases from the Web. VLDB 1999: 639-650
Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han, Teresa Mah: Exploratory Mining via Constrained Frequent Set Queries. SIGMOD Conference 1999: 556-558
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
Sunita Sarawagi, Shiby Thomas, Rakesh Agrawal: Integrating Mining with Relational Database Systems: Alternatives and Implications. SIGMOD Conference 1998: 343-354

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