Episode Rules Mining Algorithm for Distant Event Prediction
Identifieur interne : 000902 ( Main/Exploration ); précédent : 000901; suivant : 000903Episode Rules Mining Algorithm for Distant Event Prediction
Auteurs : Lina Fahed [France] ; Armelle Brun [France] ; Anne Boyer [France]Source :
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 distant events.
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Fahed, Lina" sort="Fahed, Lina" uniqKey="Fahed L" first="Lina" last="Fahed">Lina Fahed</name></region><name sortKey="Boyer, Anne" sort="Boyer, Anne" uniqKey="Boyer A" first="Anne" last="Boyer">Anne Boyer</name><name sortKey="Brun, Armelle" sort="Brun, Armelle" uniqKey="Brun A" first="Armelle" last="Brun">Armelle Brun</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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