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Mining Biclusters of Similar Values with Triadic Concept Analysis

Identifieur interne : 002049 ( Main/Exploration ); précédent : 002048; suivant : 002050

Mining Biclusters of Similar Values with Triadic Concept Analysis

Auteurs : Mehdi Kaytoue [Brésil] ; Sergei O. Kuznetsov [Russie] ; Juraj Macko [République tchèque] ; Wagner Meira [Brésil] ; Amedeo Napoli [France]

Source :

RBID : Hal:hal-00640873

English descriptors

Abstract

Biclustering numerical data became a popular data-mining task in the beginning of 2000's, especially for analysing gene expression data. A bicluster reflects a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data-table. So called biclusters of similar values can be thought as maximal sub-tables with close values. Only few methods address a complete, correct and non redundant enumeration of such patterns, which is a well-known intractable problem, while no formal framework exists. In this paper, we introduce important links between biclustering and formal concept analysis. More specifically, we originally show that Triadic Concept Analysis (TCA), provides a nice mathematical framework for biclustering. Interestingly, existing algorithms of TCA, that usually apply on binary data, can be used (directly or with slight modifications) after a preprocessing step for extracting maximal biclusters of similar values.

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Le document en format XML

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<front>
<div type="abstract" xml:lang="en">Biclustering numerical data became a popular data-mining task in the beginning of 2000's, especially for analysing gene expression data. A bicluster reflects a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data-table. So called biclusters of similar values can be thought as maximal sub-tables with close values. Only few methods address a complete, correct and non redundant enumeration of such patterns, which is a well-known intractable problem, while no formal framework exists. In this paper, we introduce important links between biclustering and formal concept analysis. More specifically, we originally show that Triadic Concept Analysis (TCA), provides a nice mathematical framework for biclustering. Interestingly, existing algorithms of TCA, that usually apply on binary data, can be used (directly or with slight modifications) after a preprocessing step for extracting maximal biclusters of similar values.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>Brésil</li>
<li>France</li>
<li>Russie</li>
<li>République tchèque</li>
</country>
<region>
<li>Grand Est</li>
<li>Lorraine (région)</li>
<li>Minas Gerais</li>
</region>
<settlement>
<li>Belo Horizonte</li>
<li>Nancy</li>
</settlement>
<orgName>
<li>Institut national polytechnique de Lorraine</li>
<li>Université Nancy 2</li>
<li>Université de Lorraine</li>
<li>Université fédérale du Minas Gerais</li>
</orgName>
</list>
<tree>
<country name="Brésil">
<region name="Minas Gerais">
<name sortKey="Kaytoue, Mehdi" sort="Kaytoue, Mehdi" uniqKey="Kaytoue M" first="Mehdi" last="Kaytoue">Mehdi Kaytoue</name>
</region>
<name sortKey="Meira, Wagner" sort="Meira, Wagner" uniqKey="Meira W" first="Wagner" last="Meira">Wagner Meira</name>
</country>
<country name="Russie">
<noRegion>
<name sortKey="Kuznetsov, Sergei O" sort="Kuznetsov, Sergei O" uniqKey="Kuznetsov S" first="Sergei O." last="Kuznetsov">Sergei O. Kuznetsov</name>
</noRegion>
</country>
<country name="République tchèque">
<noRegion>
<name sortKey="Macko, Juraj" sort="Macko, Juraj" uniqKey="Macko J" first="Juraj" last="Macko">Juraj Macko</name>
</noRegion>
</country>
<country name="France">
<region name="Grand Est">
<name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name>
</region>
</country>
</tree>
</affiliations>
</record>

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