Sets of Contrasting Rules: A Supervised Descriptive Rule Induction Pattern for Identification of Trigger Factors

Data mining, through association rules mining, is one of the best known approaches for patterns identification. However, it results most of the time in a huge set of patterns (rules), so their exploitation is not easy and often requires expert analysis. In this paper we describe a new pattern "set of contrasting rules" which, contrary to most state-of-the-art patterns, has the characteristic of being made up of a set of rules. It has also the advantage of not only identifying a reduced set of rules, but also structuring it into sets. One main originality of this pattern is that it allows to automatically identify trigger factors: factors that can bring some event state changes. In this work we show that the proposed pattern methodologically belongs to the supervised descriptive rules induction paradigm. We also show through the experiments on a real dataset of census data that "set of contrasting rules" can be considered as a way to filter the huge amount of association rules and can be used to identify trigger factors.