The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Mining for strong negative associations in a large database of customer transactions
Abstract
Mining for association rules is considered an important data mining problem. Many different variations of this problem have been described in the literature. We introduce the problem of mining for negative associations. A naive approach to finding negative associations leads to a very large number of rules with low interest measures. We address this problem by combining previously discovered positive associations with domain knowledge to constrain the search space such that fewer but more interesting negative rules are mined. We describe an algorithm that efficiently finds all such negative associations and present the experimental results.
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Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,419
Knowledge Discovery in Databases
Piateski, G., & Frawley, W. (1991) · The MIT Press eBooks · Cited by 1,749
Mining generalized association rules
Srikant, R., & Agrawal, R. (1997) · Future Generation Computer Systems · doi:10.1016/s0167-739x(97)00019-8 · Cited by 1,618
An Efficient Algorithm for Mining Association Rules in Large Databases
Savasere, A., Omiecinski, E. R., & Navathe, S. B. (1995) · Cited by 1,590
Self-citationAn effective hash-based algorithm for mining association rules
Park, J. S., Chen, M.-S., & Yu, P. S. (1995) · Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95 · doi:10.1145/223784.223813 · Cited by 1,406
Discovery, Analysis, and Presentation of Strong Rules
Piatetsky-Shapiro, G. (1991) · Medical Entomology and Zoology · Cited by 993
Discovery of Multiple-Level Association Rules from Large Databases
Han, J., & Fu, Y. (1995) · Cited by 922
Efficient algorithms for discovering association rules
Mannila, H., Toivonen, H. T. T., & Verkamo, A. I. (1994) · Cited by 624
Set-oriented mining for association rules in relational databases
Houtsma, M., & Swami, A. (2002) · Proceedings of the Eleventh International Conference on Data Engineering · doi:10.1109/icde.1995.380413 · Cited by 263
Cited by
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Frequent itemset mining: A 25 years review
Luna, J. M., Fournier‐Viger, P., & Ventura, S. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1329 · Cited by 258
Metadata
- Authors
- Savasere, A., Omiecinski, E., & Navathe, S.
- Venue
- Proceedings 14th International Conference on Data Engineering
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-27
- Pages
- 494-502
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W1892399053
- Cited by
- 268
- References
- 12
- FWCI
- 11.74
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Savasere, A., Omiecinski, E., & Navathe, S. (2002). Mining for strong negative associations in a large database of customer transactions. Proceedings 14th International Conference on Data Engineering. https://doi.org/10.1109/icde.1998.655812
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