Subgroup discovery is a broadly applicable descriptive data mining technique for identifying
interesting subgroups according to some property of interest. This article summarizes …

Rule sets are highly interpretable logical models in which the predicates for decision are
expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model …

Big data streams are possibly one of the most essential underlying notions. However, data
streams are often challenging to handle owing to their rapid pace and limited information …

The discovery of patterns that accurately discriminate one class label from another remains
a challenging data mining task. Subgroup discovery (SD) is one of the frameworks that …

Clustering is an unsupervised process which aims to discover regularities and underlying
structures in data. Constrained clustering extends clustering in such a way that expert …

We introduce the problem of robust subgroup discovery, ie, finding a set of interpretable
descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are …

Current decision trees such as C4. 5 and CART are widely used in different fields due to
their simplicity, accuracy and intuitive interpretation. Similar to other popular classifiers …

Subgroup discovery (SD) is the task of discovering interpretable patterns in the data that
stand out wrt some property of interest. Discovering patterns that accurately discriminate a …

Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration,
the selection of patterns constitutes a major challenge. The Minimum Description Length …

“I'm an MC still as honest”–Eminem, Rap God We present MCRapper, an algorithm for
efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families …