Constraint-Based Rule Mining in Large, Dense Databases

by: Roberto Bayardo, Rakesh Agrawal, Dimitrios Gunopulos

Data Mining and Knowledge Discovery, Vol. 4, No. 2. (10 July 2000), pp. 217-240.

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Abstract

Abstract Constraint-based rule miners find all rules in a given data-set 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 tables). Previous approaches such as Apriori and its variants exploit only the minimum support constraint, and as a result are ineffective on dense data due to a combinatorial explosion of “frequent itemsets”.

@article{citeulike:1774739, abstract = {Abstract\ \ Constraint-based rule miners find all rules in a given data-set 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 tables). Previous approaches such as Apriori and its variants exploit only the minimum support constraint, and as a result are ineffective on dense data due to a combinatorial explosion of “frequent itemsets”.}, author = {Bayardo, Roberto and Agrawal, Rakesh and Gunopulos, Dimitrios }, citeulike-article-id = {1774739}, doi = {http://dx.doi.org/10.1023/A:1009895914772}, journal = {Data Mining and Knowledge Discovery}, keywords = {constraints, mining, rules}, month = {July}, number = {2}, pages = {217--240}, posted-at = {2008-05-05 10:51:17}, priority = {2}, title = {Constraint-Based Rule Mining in Large, Dense Databases}, url = {http://dx.doi.org/10.1023/A:1009895914772}, volume = {4}, year = {2000} }

RIS record

TY - JOUR ID - citeulike:1774739 L3 - citeulike-article-id:1774739 TI - Constraint-Based Rule Mining in Large, Dense Databases JF - Data Mining and Knowledge Discovery VL - 4 IS - 2 SP - 217 EP - 240 N2 - Abstract  Constraint-based rule miners find all rules in a given data-set 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 tables). Previous approaches such as Apriori and its variants exploit only the minimum support constraint, and as a result are ineffective on dense data due to a combinatorial explosion of “frequent itemsets”. KW - constraints KW - mining KW - rules AU - Bayardo, Roberto AU - Agrawal, Rakesh AU - Gunopulos, Dimitrios PY - 2000/07/10/ UR - http://dx.doi.org/10.1023/A:1009895914772 ER -

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