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Data Mining consists in extracting previously unknown, potentially useful and reliable patterns from a given
set of data. The use of classification techniques, as well as the use of clustering techniques, can
provide important information for applications arising in various disciplines. The aim of our
Classification Days is to bring together researchers either working on the development
of specific data mining techniques, or interested in using such techniques for extracting useful
information from databases obtained when studying particular real-life problems.
During each CD, speakers are invited to give presentations of their scientific results and of their
scientific projects; these presentations will then be used as a starting point for a deeper
discussion involving all partecipants. At each CD, previous discussions are reminded and, when
this is the case, new collaborations issued from previous CDs are presented.
This page is for collecting all CD's programs, discussions and slides provided by the speakers
(just click on presentation titles to get the slides). It also collects the main references at the
basis of the discussions, with a direct link to the original publication.
Important:
PhD students of the Matisse Doctoral School, with the agreement of their supervisors, can have
their attendance to the CDs validated as "heures de formation".
The discussions started during our CD1 lead to a Master internship (level 1, 2 months) at University
of Rennes 1, whose main results are summarized in the conference paper:
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F. Elain, A. Mucherino, L. Hoyet, R. Kulpa,
Feature Selection in Time-Series Motion Databases,
IEEE Conference Proceedings,
Federated Conference on Computer Science and Information Systems (FedCSIS18),
Workshop on Computational Optimization (WCO18),
Poznan, Poland, 245-248, 2018.
Larger events focusing on these topics have successively been organized at INRIA:
Program of the Classification Days:
CD4 "Deep Learning", October 17th, 2017.
CD3 "Time-series classification", June 28th, 2017.
CD2 "Enhancing k-means", February 7th, 2017.
CD1 "Supervised biclustering", November 10th, 2016.
Main bibliography
- k-means
- Supervised biclustering
S. Busygin, O.A. Prokopyev, P.M. Pardalos,
Feature Selection for Consistent Biclustering via Fractional 0-1 Programming,
Journal of Combinatorial Optimization 10, 7-21, 2005.
A. Mucherino, L. Liberti,
A VNS-based Heuristic for Feature Selection in Data Mining.
In: "Hybrid Meta-Heuristics", Studies in Computational Intelligence 434,
E-G. Talbi (Ed.), 353-368, 2013.
- Big data approaches
N. Keriven, A. Bourrier, R. Gribonval, P. Pérez,
Sketching for Large-Scale Learning of Mixture Models,
IEEE Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP2016),
Shanghai, China, 6 pages, 2016.
N. Keriven, N. Tremblay, Y. Traonmilin, R. Gribonval,
Compressive k-means,
IEEE Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP2017),
New Orleans, USA, 5 pages, 2017.
- Time series classification
A. Bagnall, J. Lines, A. Bostrom, J. Large, E. Keogh,
The Great Time Series Classification Bake Off: a Review and Experimental Evaluation of Recent Algorithmic Advances,
Data Mining and Knowledge Discovery 31(3), 606-660, 2017.
A. Bailly, S. Malinowski, R. Tavenard, L. Chapel, T. Guyet,
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification,
Lecture Notes in Artificial Intelligence 9785, 17-30, 2016.
- Applications
F. Argelaguet, M. Ducoffe, A. Lécuyer, R. Gribonval,
Spatial and Rotation Invariant 3D Gesture Recognition Based on Sparse Representation,
IEEE Symposium on 3D User Interfaces (3DUI),
Los Angeles, CA, USA, 10 pages, March 2017.
A.L. Cruz Ruiz, C. Pontonnier, A. Sorel, G. Dumont,
Identifying Representative Muscle Synergies in Overhead Football Throws,
Computer Methods in Biomechanics and Biomedical Engineering 18(sup1), 1918-1919, 2015.
B. Merabti, M. Christie, K. Bouatouch,
A Virtual Director using Hidden Markov Models,
Computer Graphics Forum 35(8), 51-67, 2016.
M. Morel, R. Kulpa, A. Sorel, C. Achard, S. Dubuisson,
Automatic and Generic Evaluation of Spatial and Temporal Errors in Sport Motions,
Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications,
Rome, Italy, 542-551, 2016.
H.Y. Wu, M. Christie,
Analysing Cinematography with Embedded Constrained Patterns,
Proceedings of Eurographics 2016.
Software
External links
Organization
The CDs are organized by Antonio Mucherino and the main participants are the members of the MimeTIC team at IRISA/INRIA.
However, the speakers from other teams are regurarly invited to give presentations and take part to the discussions.
Thanks
To INRIA for providing the coffee breaks!
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Classification Days
