Data mining evolved as a collection of applicative problems and efficient solution algorithms relative to rather peculiar problems, all focused on the discovery of relevant information hidden in databases of huge dimensions. In particular, one of the most investigated topics is the discovery of association rules. This work proposes a unifying model that enables a uniform description of the problem of discovering association rules. The model provides SQL-like operator, named MINE RULE, which is capable of expressing all the problems presented so far in the literature concerning the mining of association rules. We demonstrate the expressive power of the new operator by means of several examples, some of which are classical, while some others are fully original and correspond to novel and unusual applications. We also present the operational semantics of the operator by means of an extended relational algebra.

A New SQL-like Operator for Mining Association Rules

MEO, Rosa;
1996

Abstract

Data mining evolved as a collection of applicative problems and efficient solution algorithms relative to rather peculiar problems, all focused on the discovery of relevant information hidden in databases of huge dimensions. In particular, one of the most investigated topics is the discovery of association rules. This work proposes a unifying model that enables a uniform description of the problem of discovering association rules. The model provides SQL-like operator, named MINE RULE, which is capable of expressing all the problems presented so far in the literature concerning the mining of association rules. We demonstrate the expressive power of the new operator by means of several examples, some of which are classical, while some others are fully original and correspond to novel and unusual applications. We also present the operational semantics of the operator by means of an extended relational algebra.
IEEE International Conference on Very Large Data Bases
Bombay, India
SEPTEMBER 1996
Proceedings of 22th International Conference on Very Large Data Bases
Morgan Kaufmann
22
122
133
9781558603820
http://www.informatik.uni-trier.de/~ley/db/conf/vldb/vldb96.html
R. MEO; G. PSAILA; S. CERI
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/18591
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