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CORON: A Framework for Levelwise Itemset Mining Algorithms

Identifieur interne : 005B02 ( Main/Exploration ); précédent : 005B01; suivant : 005B03

CORON: A Framework for Levelwise Itemset Mining Algorithms

Auteurs : Laszlo Szathmary [France] ; Amedeo Napoli [France]

Source :

RBID : Hal:inria-00001201

English descriptors

Abstract

CORON is a framework for levelwise algorithms that are designed to find frequent and/or frequent closed itemsets in binary contexts. Datasets can be very different in size, number of objects, number of attributes, density, etc. As there is no one best algorithm for arbitrary datasets, we want to give a possibility for users to try different algorithms and choose the one that best suits their needs.

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Le document en format XML

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</hal:affiliation>
<country>France</country>
<placeName>
<settlement type="city">Nancy</settlement>
<region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
</placeName>
<orgName type="university">Université Nancy 2</orgName>
<orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName>
<placeName>
<settlement type="city">Nancy</settlement>
<region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
</placeName>
<orgName type="university">Institut national polytechnique de Lorraine</orgName>
<orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName>
</affiliation>
</author>
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<textClass>
<keywords scheme="mix" xml:lang="en">
<term>algorithms</term>
<term>association rules</term>
<term>closed frequent itemsets</term>
<term>data mining</term>
<term>frequent itemsets</term>
<term>itemset mining</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">CORON is a framework for levelwise algorithms that are designed to find frequent and/or frequent closed itemsets in binary contexts. Datasets can be very different in size, number of objects, number of attributes, density, etc. As there is no one best algorithm for arbitrary datasets, we want to give a possibility for users to try different algorithms and choose the one that best suits their needs.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>France</li>
</country>
<region>
<li>Grand Est</li>
<li>Lorraine (région)</li>
</region>
<settlement>
<li>Nancy</li>
</settlement>
<orgName>
<li>Institut national polytechnique de Lorraine</li>
<li>Université Nancy 2</li>
<li>Université de Lorraine</li>
</orgName>
</list>
<tree>
<country name="France">
<region name="Grand Est">
<name sortKey="Szathmary, Laszlo" sort="Szathmary, Laszlo" uniqKey="Szathmary L" first="Laszlo" last="Szathmary">Laszlo Szathmary</name>
</region>
<name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name>
</country>
</tree>
</affiliations>
</record>

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