Episode Rules Mining Algorithm for Distant Event Prediction

Lina Fahed (1) , Armelle Brun (1) , Anne Boyer (1)

1 KIWI - Knowledge Information and Web Intelligence

This paper focuses on event prediction in an event sequence, where we aim at predicting distant events. We propose an algorithm that mines episode rules, which are minimal and have a consequent temporally distant from the antecedent. As traditional algorithms are not able to mine directly rules with such characteristics, we propose an original way to mine these rules. Our algorithm, which has a complexity similar to that of state of the art algorithms, determines the consequent of an episode rule at an early stage in the mining process, it applies a span constraint on the antecedent and a gap constraint between the antecedent and the consequent. A new confidence measure, the temporal confidence, is proposed, which evaluates the confidence of a rule in relation to the predefined gap. The algorithm is validated on an event sequence of social networks messages. We show that minimal rules with a distant consequent are actually formed and that they can be used to accurately predict distant events.

Mots clés: Data Mining; Episode Rules Mining; Minimal Rules; Distant Event Prediction

Domaines: Autre [cs.OH]

Lina Fahed, Armelle Brun, Anne Boyer. Episode Rules Mining Algorithm for Distant Event Prediction. [Research Report] 2014. ⟨hal-01062542⟩