Massive Pruning for Building an Operational Set of Association Rules: Metarules for Eliminating Conflicting and Redundant Rules.
Identifieur interne : 003705 ( Main/Exploration ); précédent : 003704; suivant : 003706Massive Pruning for Building an Operational Set of Association Rules: Metarules for Eliminating Conflicting and Redundant Rules.
Auteurs : Martine Cadot [France] ; Alain Lelu [France]Source :
English descriptors
- mix :
Abstract
Extracting a set of Association Rules (AR) is a common method for representing knowledge embedded in a database. As long as many authors have aimed at improving the individual quality of these rules, not so many have considered their global quality and cohesiveness: Our objective is to provide the user with a set of rules he/she may combine to reason with, a consistent set as regards to “common sense logic”. As local quality measures offer no warranty in this respect, we have defined patterns of major incoherencies and have associated metarules to them, resulting in a post-treatment cleaning phase for tracking down incoherencies and proposing corrections. We show that on the artificial Lucas0 database of the Causality Challenge [11], starting from 100 000 rules, we have reduced this rule set by three orders of magnitude, to 69 high-quality condensed rules embedding most of the structure designed by the challenge organizers.
Url:
Affiliations:
- France
- Franche-Comté, Grand Est, Lorraine (région)
- Besançon, Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Bourgogne Franche-Comté, Université de Franche-Comté, Université de Lorraine
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Le document en format XML
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Franche-Comté</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Association Rules</term><term>Commun Sense Logic</term><term>Data Mining</term><term>Knowledge discovery</term><term>Knowledge extraction</term><term>Machine Learning</term><term>Massive Pruning</term><term>Meta-rules</term><term>similarity of rules</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Extracting a set of Association Rules (AR) is a common method for representing knowledge embedded in a database. As long as many authors have aimed at improving the individual quality of these rules, not so many have considered their global quality and cohesiveness: Our objective is to provide the user with a set of rules he/she may combine to reason with, a consistent set as regards to “common sense logic”. As local quality measures offer no warranty in this respect, we have defined patterns of major incoherencies and have associated metarules to them, resulting in a post-treatment cleaning phase for tracking down incoherencies and proposing corrections. We show that on the artificial Lucas0 database of the Causality Challenge [11], starting from 100 000 rules, we have reduced this rule set by three orders of magnitude, to 69 high-quality condensed rules embedding most of the structure designed by the challenge organizers.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Franche-Comté</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Besançon</li><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Bourgogne Franche-Comté</li><li>Université de Franche-Comté</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Cadot, Martine" sort="Cadot, Martine" uniqKey="Cadot M" first="Martine" last="Cadot">Martine Cadot</name></region><name sortKey="Lelu, Alain" sort="Lelu, Alain" uniqKey="Lelu A" first="Alain" last="Lelu">Alain Lelu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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