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This paper presents a method for extracting association rules by means of a multi-objective grammar guided ant programming algorithm. Solution construction is guided by a context-free grammar specifically suited for association rule mining, which defines the search space of all possible expressions or programs. Evaluation of individuals is considered from a(More)
This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by(More)
Rare association rules are those that only appear infrequently even though they are highly associated with very specific data. In consequence, these rules can be very appropriate for using with educational datasets since they are usually imbalanced. In this paper, we explore the extraction of rare association rules when gathering student usage data from a(More)
Association rule mining is a well-known data mining task, but it requires much computational time and memory when mining large scale data sets of high dimensionality. This is mainly due to the evaluation process, where the antecedent and consequent in each rule mined are evaluated for each record. This paper presents a novel methodology for evaluating(More)
This paper treats the first approximation to the extraction of association rules by employing ant programming, a technique that has recently reported very promising results in mining classification rules. In particular, two different algorithms are presented, both guided by a context-free grammar, specifically suited to association rule mining, which(More)