InforLorV4, Hal, Corpus, bibRecord, 003210

Mining Heterogeneous Multidimensional Sequential Patterns

Identifieur interne : 003210 ( Hal/Corpus ); précédent : 003209; suivant : 003211

Mining Heterogeneous Multidimensional Sequential Patterns

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

Source :

RBID : Hal:hal-01094365

Abstract

All domains of science and technology produce large and hetero-geneous data. Although much work has been done in this area, min-ing 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 multidi-mensional 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-01094365

DOI: 10.3233/978-1-61499-419-0-279

Links to Exploration step

Hal:hal-01094365

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 hetero-geneous data. Although much work has been done in this area, min-ing 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 multidi-mensional 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 hetero-geneous data. Although much work has been done in this area, min-ing 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 multidi-mensional 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>
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Mining Heterogeneous Multidimensional Sequential Patterns

Mining Heterogeneous Multidimensional Sequential Patterns

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