An evolutionary fuzzy system for the detection of exceptions in subgroup discovery

Subgroup Discovery (SD) is a data mining technique whose main objective is the search for descriptions of subgroups of data that are statistically unusual with respect to a property of interest. General rules describing as many instances as possible are preferred in SD, but this can lead to less accurate descriptions that incorrectly describe some instances. These negative examples can be grouped into exceptions. The paper presents a new evolutionary fuzzy system for the detection of exceptions associated to rules previously obtained by a SD algorithm. Considering the initial subgroup and associated exceptions, the aim is to obtain a new description in order to increase the accuracy of the initial subgroup. This algorithm can be applied to the results of any SD algorithm. An experimental study shows the utility of the proposal, which is also applied in a real problem related to concentrating photovoltaic technology, providing useful information to the experts.