On Mining Complex Sequential Data by Means of FCA and Pattern Structures
Identifieur interne : 000696 ( Main/Exploration ); précédent : 000695; suivant : 000697On Mining Complex Sequential Data by Means of FCA and Pattern Structures
Auteurs : Aleksey Buzmakov [France] ; Elias Egho [France] ; Nicolas Jay [France] ; Sergei O. Kuznetsov [Russie] ; Amedeo Napoli [France] ; Chedy Raïssi [France]Source :
Abstract
Nowadays data sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of Formal Concept Analysis (FCA) and its extension based on "pattern structure". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e., a data reduction of sequential structures), are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analyzing interesting patient patterns from a French healthcare data set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use case which is the main motivation for this work.
Url:
DOI: 10.1080/03081079.2015.1072925
Affiliations:
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Le document en format XML
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<idno type="DOI">10.1080/03081079.2015.1072925</idno>
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<front><div type="abstract" xml:lang="en">Nowadays data sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of Formal Concept Analysis (FCA) and its extension based on "pattern structure". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e., a data reduction of sequential structures), are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analyzing interesting patient patterns from a French healthcare data set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use case which is the main motivation for this work.</div>
</front>
</TEI>
<affiliations><list><country><li>France</li>
<li>Russie</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="Buzmakov, Aleksey" sort="Buzmakov, Aleksey" uniqKey="Buzmakov A" first="Aleksey" last="Buzmakov">Aleksey Buzmakov</name>
</region>
<name sortKey="Egho, Elias" sort="Egho, Elias" uniqKey="Egho E" first="Elias" last="Egho">Elias Egho</name>
<name sortKey="Jay, Nicolas" sort="Jay, Nicolas" uniqKey="Jay N" first="Nicolas" last="Jay">Nicolas Jay</name>
<name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name>
<name sortKey="Raissi, Chedy" sort="Raissi, Chedy" uniqKey="Raissi C" first="Chedy" last="Raïssi">Chedy Raïssi</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>
</tree>
</affiliations>
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