A huge amount of data is currently being made available for exploration and analysis in many application domains. Patterns and models are extracted from data to describe their characteristics and predict variable values. Unfortunately, many high quality models are characterized by being hardly interpretable. Rules mined from data may provide easily interpretable knowledge, both for exploration and classification (or prediction) purposes. In this talk I will introduce different types of rules (e.g., several variations on association rules, classification rules) and will discuss their capability of describing phenomena and highlighting interesting correlations in data.
Read more at http://2017.ruleml.org/