One of the most important obstacles to deploying predictive models is the fact that humans
do not understand and trust them. Knowing which variables are important in a model's …

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 …

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life
Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent …

The task of detecting common and unique characteristics among different cancer subtypes is
an important focus of research that aims to improve personalized therapies. Unlike current …

Discriminative patterns can provide valuable insights into data sets with class labels, that
may not be available from the individual features or the predictive models built using them …

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 …

Finding small homogeneous subgroup cohorts in large heterogeneous populations is a
critical process for hypothesis development in biomedical research. Concurrent …

We devised an algorithm, inspired by contrast-set mining algorithms such as STUCCO, to
automatically find statistically significant properties (correlations) in crash groups. Many …

Discriminative patterns are association patterns that occur with disproportionate frequency in
some classes versus others, and have been studied under names such as emerging …