Members
Overall Objectives
Research Program
Application Domains
Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
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Section: Partnerships and Cooperations

International Initiatives

Participation In International Programs

Facepe Inria Project: CM2ID

Participants : Amedeo Napoli [contact person] , Chedy Raïssi.

This research project called “Combining Numerical and Symbolical Methods for the Classification of Multi-valued and Interval Data (CM2ID)” involves the Orpailleur Team at Inria NGE, AxIS at Inria Rocquencourt (Yves Lechevallier) and the computer science laboratory of the University of Recife (Prof. Francisco de A.T. de Carvalho). The project aims at developing and comparing classification and clustering algorithms for interval and multi-valued data. Two families of algorithms are studied, namely “clustering algorithms” based on the use of a similarity or a distance for comparing the objects, and “classification algorithms in Formal Concept Analysis (FCA)” based on attribute sharing between objects. The objectives here are to combine the facilities of both families of algorithms for improving the potential of each family in dealing with more complex and voluminous datasets.

Finally, a workshop was organized in April 2013 , namely the “French-Brazilian Workshop on Numerical and Symbolic Methods of Data Analysis -WFB2013” (http://www.cin.ufpe.br/~wfb2013/ ).

Fapemig Inria Project: IKMSDM

Participants : Amedeo Napoli [contact person] , Chedy Raïssi.

This Fapemig – Inria research project, called “Incorporating knowledge models into scalable data mining algorithms” involves researchers at Universidade Federal de Minas Gerais in Belo Horizonte –a group led by Prof. Wagner Meira– and the Orpailleur team at Inria Nancy Grand Est. In this project we are interested in the mining of large amount of data and we target two relevant application scenarios where such issue may be observed. The first one is text mining, i.e. extracting knowledge from texts and document categorization. The second application scenario is graph mining, i.e. determining relationship-based patterns and use these relations to perform classification tasks. In both cases, the computational complexity is large either because the high dimensionality of the data or the complexity of the patterns to be mined. Loïc Cerf from UFMG visited the Orpailleur team in January 2013 while Chedy Raïssi visited UFMG in May 2013.

Pronex Brasilia

Participant : Bernard Maigret [contact person] .

In this research project, the goal is to identify, using virtual screening techniques that we developed, new compounds against tropical diseases (e.g. trypanosome, dengue and mycosis) in collaboration with several Brazilian laboratories among which the Department of Biology at University of Brasilia, together with the Harmonic Pharma start-up. Through this collaboration, several PhD and postdocs came to the lab for one year training with our home-developed virtual screening engine (VSM-G). This project is in part supported by the Brazilian CNPq agency. Fruitful results were already obtained leading to several papers in preparation and patents. These patents concern the discovery of new putative treatment of strong mycosis due to fungi particularly virulent in South America. These patents were funded by the University of Brasilia, Embrapa and Harmonic Pharma.

International collaborations in Mining complex data

Participants : Mehwish Alam, Aleksey Buzmakov, Melisachew Chekol, Victor Codocedo, Adrien Coulet, Elias Egho, Ioanna Lykourentzou, Amedeo Napoli [contact person] , Chedy Raïssi, Jean-Sébastien Sereni, Mario Valencia.

PICS CNRS CAdOE

A collaboration involves the Orpailleur team, “Université du Québec à Montréal” (UQAM) in Montréal with Prof. Petko Valtchev and Laboratoire LIRMM in Montpellier with Prof. Marianne Huchard. This collaboration is supported by a CNRS PICS project (2011-2014), which is called “Concept Analysis driving Ontology Engineering” and abbreviated in “CAdOE”. The research work within this project is aimed at defining and implementing a semi-automatic methodology supporting ontology engineering based on the joint use of Formal Concept Analysis (FCA) and Relational Concept Analysis (RCA). This year the work was mainly focused on RCA and some important papers were published [33] , [57] .

Miscellaneous