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An approach to discovering temporal association rules
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Proceedings of the 2000 ACM symposium on Applied computing - Volume 1 table of contents
Como, Italy
Pages: 294 - 300  
Year of Publication: 2000
ISBN:1-58113-240-9
Authors
Juan M. Ale  Facultad de Ciencias Exactas, UNLP, Argentina, and UNLM, Ambrosetti 255 -(1405)Buenos Aires, Argentina
Gustavo H. Rossi  LIFIA, Facultad de Informática, UNLP, Argentina and CONICET and UNLM, Calles 1 y 49 - La Plata, Argentina
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 13,   Downloads (12 Months): 112,   Citation Count: 13
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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Agrawal, R.-Srikent, R.: Fast Algorithms for Mining Association Rules. IBM Res. Rep. RJ9839, IBM Almaden. June 1994.
 
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Chert, X.-Petrounias, l.-Heathfielci,H.: Discovering Temporal Association Rules in Temporal Databases. Proc. Int'l Workshop IADT'98. July 1998.
 
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Kimball, Ralph: The Data Warehouse Toolkit. John Wiley & Sons. 1996.
 
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Mannila, H.-Toivonen, H.-Verkamo, I: Discovering Frequent Episodes in Sequences. KDD'95. AAAI: 210-215. August 1995.
 
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Tansel, A.-Ayan, N.: Discovery of Association Rules in Temporal Databases. Fourth lnt'l Conference on KDD Workshop on Distributed Data Mining. August 1998.
 
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CITED BY  13

Collaborative Colleagues:
Juan M. Ale: colleagues
Gustavo H. Rossi: colleagues