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Claude Pasquier
Chargé de recherche au CNRS
100%
Libre accès
11
Documents
Affiliations actuelles
- 13009
Identifiants chercheurs
- claudepasquier
- Arxiv : pasquier_c_1
- ResearcherId : C-6163-2008
- 0000-0001-7498-395X
- Google Scholar : https://scholar.google.fr/citations?user=YxoGQlYAAAAJ
- IdRef : 057733023
- ResearcherId : http://www.researcherid.com/rid/C-6163-2008
Site web
- https://www.i3s.unice.fr/~cpasquie
Présentation
Claude Pasquier is researcher at French National Center for Scientific Research (CNRS).
He received a Ph.D. degree in Computer Science from the University of Nice - Sophia Antipolis (now Université Côte d’Azur), France, in 1994. During his thesis, he explored the use of software engineering paradigms in the field of structured document manipulation. He defined both a frame-based language for representing document models, and a system of context-aware document generation and configuration (cf. Prototype-based programming.
Subsequently, he was a postdoctoral researcher at the Biophysics and Bioinformatics Laboratory of the University of Athens, Greece, where he conducted research on protein structure prediction.
He held positions at the National Institute for Research in Digital Science and Technology (INRIA) and with Schlumberger, Smart Cards & Terminals division (now Gemalto) where he worked on generative programming.
Since 2002 when he joined CNRS, he successively worked at Villefranche Oceanographic Laboratory (LOV), the Institute of Biology Valrose (iBV) and New Caledonia Institute of Exact and Applied Sciences (ISEA) where he addressed topics as diverse as semantic data integration, omics data mining and attributed graph mining.
Currently at I3S laboratory, he is conducting research focused on complex network mining that combines computer science and systems biology.
Publications
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Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic GraphKnowledge and Information Systems (KAIS), 2020, 62 (10), pp.3931-3971. ⟨10.1007/s10115-020-01478-9⟩
Article dans une revue
hal-02909702v1
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Attributed graph mining in the presence of automorphismKnowledge and Information Systems (KAIS), 2017, 50 (2), pp.569-584. ⟨10.1007/s10115-016-0953-9⟩
Article dans une revue
hal-01311684v1
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Frequent Pattern Mining in Attributed Trees: algorithms and applicationsKnowledge and Information Systems (KAIS), 2015, 46 (3), pp.491-514. ⟨10.1007/s10115-015-0831-x⟩
Article dans une revue
hal-01154854v1
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Les modèles des experts au service de l'extraction de motifs pertinentsReconnaissance de Formes et Intelligence Artificielle (RFIA) 2014, Jun 2014, ROUEN, France
Communication dans un congrès
hal-00989223v1
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Extraction de motifs dans des graphes orientés attribués en présence d’automorphisme14e Conférence Francophone sur l'Extraction et la Gestion des Connaissances (EGC'14), Jan 2014, Rennes, France
Communication dans un congrès
hal-01151511v1
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Improving pattern discovery relevancy by deriving constraints from expert modelsEuropean Conference on Artificial Intelligence (ECAI'2014), Aug 2014, Prague, Czech Republic. pp.327 - 332, ⟨10.3233/978-1-61499-419-0-327⟩
Communication dans un congrès
hal-01151514v1
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Extraction de motifs fréquents dans des arbres attribués13e Conférence Francophone sur l'Extraction et la Gestion des Connaissances (EGC'13), Jan 2013, Toulouse, France. pp.193-204
Communication dans un congrès
hal-01151512v1
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Extraction de motifs condensés dans un unique graphe orienté acyclique attribué13e Conférence Francophone sur l'Extraction et la Gestion des Connaissances (EGC'13), Jan 2013, Toulouse, France
Communication dans un congrès
hal-01151510v1
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Weighted Path as a Condensed Pattern in a Single Attributed DAG23rd International Joint Conference on Artificial Intelligence (IJCAI'13), Aug 2013, Beijing, China. pp.1642-8
Communication dans un congrès
hal-01151561v1
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Frequent Pattern Mining in Attributed trees17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'13), Apr 2013, Gold Coast, Australia. pp.26-37, ⟨10.1007/978-3-642-37453-1_3⟩
Communication dans un congrès
hal-01151513v1
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