Subgroup discovery is a data mining technique which extracts interesting rules with respect
to a target variable. An important characteristic of this task is the combination of predictive …

Subgroup discovery (SD) is a descriptive data mining technique using supervised learning.
In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will …

Subgroup discovery is a Knowledge Discovery task that aims at finding subgroups of a
population with high generality and distributional unusualness. While several subgroup …

Subgroup discovery is a data mining technique that discovers interesting associations
among different variables with respect to a property of interest. Existing subgroup discovery …

We study the problem of designing privacy-enhanced solutions for interest-based
advertisement (IBA). IBA is a key component of the online ads ecosystem and provides a …

Existing algorithms for subgroup discovery with numerical targets do not optimize the error
or target variable dispersion of the groups they find. This often leads to unreliable or …

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 …

We address the problem of building a clustering as a subset of a (possibly large) set of
candidate clusters under user-defined constraints. In contrast to most approaches to …

Data mining is one of the interesting research areas in database technology. In data mining,
a cluster is a set of data objects that are similar to one another with in a cluster and are …

Patient data in clinical research often includes large amounts of structured information, such
as neuroimaging data, neuropsychological test results, and demographic variables. Given …