Rule learning has a long history within the field of machine learning. In particular, the so-called separate-and-conquer or covering family of rule-based classification algorithms goes back to the early days of machine learning. Since the first papers on the AQ rule learning algorithm, this research area has had its ups and downs but it never completely vanished from the research menus of our field. The primary reason for this lies in the attractiveness of rules as the arguably most comprehensive concept representation formalism. After its peak in the early nineties (through the advent of Inductive Logic Programming algorithms), the focus of research in rule learning soon shifted to association rule discovery, and interest in inductive rule learning declined. Lately, we can observe another increase of interest in this area. Recent advances include novel methods for handling contradicting or missing predictions, multiinstance rule learning, subgroup discovery, integration of boosting and covering, covering on association rules, statistical approaches to rule-based prediction and clustering, efficient learning with rule templates, alternatives to the coverin
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