Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
JCLEC: a Java framework for evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (pp 315-357) "Ordered structures in many-valued logic"
Information Sciences: an International Journal
IEEE Transactions on Fuzzy Systems
RM-Tool: A framework for discovering and evaluating association rules
Advances in Engineering Software
Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Fuzzy Systems
An overview on subgroup discovery: foundations and applications
Knowledge and Information Systems
Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing
IEEE Transactions on Fuzzy Systems
Knowledge and Information Systems
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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.