Subgroup discovery with linguistic rules

Abstract

Subgroup discovery can be defined as a form of supervised inductive learning in which, given a population of individuals and a specific property of individuals in which we are interested, find population subgroups that have the most unusual distributional characteristics with respect to the property of interest. Subgroup discovery algorithms aim at discovering individual rules, which must be represented in explicit symbolic form and which must be simple and understandable in order to be recognized as actionable by potential users. A fuzzy approach for a subgroup discovery process, which considers linguistic variables with linguistic terms in descriptive fuzzy rules, lets us obtain knowledge in a similar way of the human thought process. Linguistic rules are naturally inclined towards coping with linguistic knowledge and to produce more interpretable and actionable solutions. This chapter analyzes the use of linguistic rules for modelling this problem, and shows a genetic extraction model for learning this kind of rules. © 2008 Springer-Verlag Berlin Heidelberg.