A brief overview of rule learning

Abstract

In this paper, we provide a brief summary of elementary research in rule learning. The two main research directions are descriptive rule learning, with the goal of discovering regularities that hold in parts of the given dataset, and predictive rule learning, which aims at generalizing the given dataset so that predictions on new data can be made. We briefly review key learning tasks such as association rule learning, subgroup discovery, and the covering learning algorithm, along with their most important prototypes. The paper also highlights recent work in rule learning on the Semantic Web and Linked Data as an important application area.