Abstract
This paper deals with the problem of discovering subgroups in data by means of a grammar guided genetic programming algorithm, each subgroup including a set of related patterns. The proposed algorithm combines the requirements of discovering comprehensible rules with the ability of mining expressive and flexible solutions thanks to the use of a context-free grammar. A major characteristic of this algorithm is the small number of parameters required, so the mining process is easy for end-users.
The algorithm proposed is compared with existing subgroup discovery evolutionary algorithms. The experimental results reveal the excellent behaviour of this algorithm, discovering comprehensible subgroups and behaving better than the other algorithms. The conclusions obtained were reinforced through a series of non-parametric tests.
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Luna, J.M., Romero, J.R., Romero, C., Ventura, S. (2013). Discovering Subgroups by Means of Genetic Programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds) Genetic Programming. EuroGP 2013. Lecture Notes in Computer Science, vol 7831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37207-0_11
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DOI: https://doi.org/10.1007/978-3-642-37207-0_11
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