Subgroup Discovery Through Sharp Transitions Using Implicative Type Rules

Subgroup Discovery is a descriptive data mining technique for obtaining subgroups with unusual statistical characteristics with respect to a given target variable. In this paper, unlike existing approaches, we capture the data in the form of implicative-type fuzzy rules and propose an algorithm to determine sharp transitions in the consequent when there is a minimal change in the antecedent. Our study contained herein highlights the role and employability of fuzzy implication functions in such settings through illustrative examples with several real datasets.