InforLorV4, Hal, Corpus, bibRecord, 003209

Mining Heterogeneous Multidimensional Sequential Patterns

Identifieur interne : 003209 ( Hal/Corpus ); précédent : 003208; suivant : 003210

Mining Heterogeneous Multidimensional Sequential Patterns

Auteurs : Elias Egho ; Chedy Raïssi ; Nicolas Jay ; Amedeo Napoli

Source :

RBID : Hal:hal-00979804

Abstract

All domains of science and technology produce large and heterogeneous data. Although much work has been done in this area, mining such data is still a challenge. No previous research targets the mining of heterogeneous multidimensional sequential data. In this work, we present a new approach to extract heterogeneous multidimensional sequential patterns with different levels of granularity by relying on external taxonomies. We show the efficiency and interest of our approach with the analysis of trajectories of care for colorectal cancer using data from the French casemix information system.

Url:

https://hal.inria.fr/hal-00979804

Links to Exploration step

Hal:hal-00979804

Le document en format XML

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<front><div type="abstract" xml:lang="en">All domains of science and technology produce large and heterogeneous data. Although much work has been done in this area, mining such data is still a challenge. No previous research targets the mining of heterogeneous multidimensional sequential data. In this work, we present a new approach to extract heterogeneous multidimensional sequential patterns with different levels of granularity by relying on external taxonomies. We show the efficiency and interest of our approach with the analysis of trajectories of care for colorectal cancer using data from the French casemix information system.</div>
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<abstract xml:lang="en">All domains of science and technology produce large and heterogeneous data. Although much work has been done in this area, mining such data is still a challenge. No previous research targets the mining of heterogeneous multidimensional sequential data. In this work, we present a new approach to extract heterogeneous multidimensional sequential patterns with different levels of granularity by relying on external taxonomies. We show the efficiency and interest of our approach with the analysis of trajectories of care for colorectal cancer using data from the French casemix information system.</abstract>
<abstract xml:lang="fr">Tous les domaines de la science et de la technologie produisent gros volumes de données hétérogènes. L'exploration de tels volumes de données reste toujours un défi. Peu de travaux ciblent l'exploration et l'analyse de données séquentielles multidimensionnelles et hétérogènes. Dans ce travail, nous présentons une nouvelle approche appelée MMISP pour extraire des motifs séquentiels à partir de données séquentielles multidimensionnelles et hétérogènes, selon différents niveaux de granularité dépendant de connaissances extérnes. L'approache MMISP a été appliquée à l'analyser de trajectoires de soins de santé de patients en oncologie. Les données proviennent d'une base de données médico-administrative incluant toutes les informations sur les hospitalisations des patients dans la région Lorraine (France).</abstract>
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</record>

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EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Hal/Corpus

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 003209 | SxmlIndent | more

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{{Explor lien
   |wiki=    Wicri/Lorraine
   |area=    InforLorV4
   |flux=    Hal
   |étape=   Corpus
   |type=    RBID
   |clé=     Hal:hal-00979804
   |texte=   Mining Heterogeneous Multidimensional Sequential Patterns
}}

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	Serveur d'exploration sur la recherche en informatique en Lorraine
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Serveur d'exploration sur la recherche en informatique en Lorraine

Mining Heterogeneous Multidimensional Sequential Patterns

Mining Heterogeneous Multidimensional Sequential Patterns

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Abstract

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