The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Constraint-based rule mining in large, dense databases
Abstract
Constraint-based rule miners find all rules in a given dataset meeting user-specified constraints such as minimum support and confidence. We describe a new algorithm that directly exploits all user-specified constraints including minimum support, minimum confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications. Our algorithm maintains efficiency even at low supports on data that is dense (e.g. relational data). Previous approaches such as Apriori and its variants exploit only the minimum support constraint, and as a result are ineffective on dense data date to a combinatorial explosion of "frequent itemsets".
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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
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Algorithms for association rule mining — a general survey and comparison
Hipp, J., Güntzer, U., & Nakhaeizadeh, G. (2000) · ACM SIGKDD Explorations Newsletter · doi:10.1145/360402.360421 · Cited by 962
H-mine: hyper-structure mining of frequent patterns in large databases
Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., & Yang, D. (2002) · Proceedings 2001 IEEE International Conference on Data Mining · doi:10.1109/icdm.2001.989550 · Cited by 408
(2003) · Cited by 165
Metadata
- Authors
- Bayardo, R. J., Agrawal, R., & Gunopulos, D.
- Venue
- Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337)
- Publisher
- IEEE
- Published
- 1999
- Pages
- 188-197
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2126400629
- Cited by
- 362
- References
- 1
- FWCI
- 29.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bayardo, R. J., Agrawal, R., & Gunopulos, D. (1999). Constraint-based rule mining in large, dense databases. Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337). https://doi.org/10.1109/icde.1999.754924
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