Mining Heterogeneous Multidimensional Sequential Patterns

Elias Egho 1 Chedy Raïssi 1 Nicolas Jay 1 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
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.
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Elias Egho, Chedy Raïssi, Nicolas Jay, Amedeo Napoli. Mining Heterogeneous Multidimensional Sequential Patterns. [Research Report] RR-8521, INRIA. 2014. ⟨hal-00979804⟩

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