Jacques-Henri Sublemontier
University of Orléans
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Publication
Featured researches published by Jacques-Henri Sublemontier.
international conference on data mining | 2009
Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin; Jacques-Henri Sublemontier
This paper deals with clustering for multi-view data, i.e. objects described by several sets of variables or proximity matrices. Many important domains or applications such as Information Retrieval, biology, chemistry and marketing are concerned by this problematic. The aim of this data mining research field is to search for clustering patterns that perform a consensus between the patterns from different views. This requires to merge information from each view by performing a fusion process that identifies the agreement between the views and solves the conflicts. Various fusion strategies can be applied, occurring either before, after or during the clustering process. We draw our inspiration from the existing algorithms based on a centralized strategy. We propose a fuzzy clustering approach that generalizes the three fusion strategies and outperforms the main existing multi-view clustering algorithm both on synthetic and real datasets.
international conference on data mining | 2011
Jacques-Henri Sublemontier; Lionel Martin; Guillaume Cleuziou; Matthieu Exbrayat
In this paper we introduce new models for semi-supervised clustering problem, in particular we address this problem from the representation space point of view. Given a dataset enhanced with constraints (typically must-link and cannot-link constraints) and any clustering algorithm, the proposed approach aims at learning a projection space for the dataset that satisfies not only the constraints but also the required objective of the clustering algorithm on unenhanced data. We propose a boosting framework to weight the constraints and infers successive projection spaces in such a way that algorithm performance is improved. We experiment this approach on standard UCI datasets and show the effectiveness of our algorithm.
international symposium on neural networks | 2013
Jacques-Henri Sublemontier
In this paper, we propose a collaborative framework that is able to solve multi-view and alternative clustering problems using some clustering ensemble and semi-supervised clustering principles. We provide a mechanism to control, via an information sharing model, different clustering algorithms to obtain consensus or alternative clustering solutions. The strong point is that our approach does not need to know which clustering algorithms to use nor their parameters.
XVIIIèmes Rencontres de la Société Francophone de Classification | 2011
Jacques-Henri Sublemontier; Lionel Martin; Guillaume Cleuziou; Matthieu Exbrayat
Fouille de données complexes | 2011
Jacques-Henri Sublemontier; Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin
Revue des Nouvelles Technologies de l'Information, numéro spécial Fouille de Données Complexes : données multiples | 2010
Jacques-Henri Sublemontier; Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin
Groupe de Travail EGC-FDC | 2010
Jacques-Henri Sublemontier; Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin
XVIèmes Rencontres de la Société Francophone de Classification | 2009
Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin; Jacques-Henri Sublemontier
Groupe de Travail EGC-FDC | 2009
Jacques-Henri Sublemontier; Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin
EGC 2009 | 2009
Jacques-Henri Sublemontier; Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin