Rules–the clearest, most explored and best understood form of knowledge representation–
are particularly important for data mining, as they offer the best tradeoff between human and …

This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and
subgroup discovery (SD) in a unifying framework named supervised descriptive rule …

Subgroup discovery is the task of finding subgroups of a population which exhibit both
distributional unusualness and high generality. Due to the non monotonicity of the …

This paper describes the application of evolutionary fuzzy systems for subgroup discovery to
a medical problem, the study on the type of patients who tend to visit the psychiatric …

Discriminative pattern mining is one of the most important techniques in data mining. This
challenging task is concerned with finding a set of patterns that occur with disproportionate …

The objective of subgroup discovery is to find groups of individuals who are statistically
different from others in a large data set. Most existing measures of the quality of subgroups …

The task addressed and the method proposed in this paper aim at improved understanding
of differences between similar diseases. In particular we address the problem of …

It is conventional wisdom in inductive rule learning that shorter rules should be preferred
over longer rules, a principle also known as Occam's Razor. This is typically justified with the …

Understanding differences between groups in a data set is one of the fundamental tasks in
data analysis. As relevant applications accumulate, data-mining methods have been …

Identifying differences between groups is one of the most important knowledge discovery
problems. The procedure, also known as contrast sets mining, is applied in a wide range of …