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Firstly, as for the mining of frequent association rules, a novel grammar-based algorithm, called G3PARM, has been proposed. It is able to discover rules having positive, negative, categorical and quantitative attributes. The evolutionary model is able to perform the mining process in one single step. This PhD Thesis also includes a model for mining rare or infrequent association rules, as well as two multi-objective approaches that optimise two different quality measures at time. Additionally, two novel algorithms that self-adapt their parameters are considered. In this sense, a previous tuning of the parameters would not be required, as they are adjusted depending on the data under study. Finally, the developed methodologies have been applied to the educational field to discover interesting information that could be used to improve the courses.
All the algorithms proposed in this Doctoral Thesis have been evaluated in a proper experimental framework, using different types of datasets and comparing their performance against other published methods of proved quality. Results have been verified by applying non-parametric statistical tests, demonstrating the many benefits of using a grammar-based methodology to address the association rule mining problem",
Genetic Programming entries for Jose Maria Luna