Authors:
Lina Fahed
;
Armelle Brun
and
Anne Boyer
Affiliation:
Université de Lorraine and LORIA, France
Keyword(s):
Data Mining, Episode Rules Mining, Minimal Rules, Distant Event Prediction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
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 distan
t events.
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