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Dive into the research topics where Jacques-Henri Sublemontier is active.

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Featured researches published by Jacques-Henri Sublemontier.


international conference on data mining | 2009

CoFKM: A Centralized Method for Multiple-View Clustering

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

Integrating Pairwise Constraints into Clustering Algorithms: Optimization-Based Approaches

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

Unsupervised collaborative boosting of clustering: An unifying framework for multi-view clustering, multiple consensus clusterings and alternative clustering

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

Intégration de contraintes must-link et cannot-link pour la classification : une approche indépendante de l'algorithme

Jacques-Henri Sublemontier; Lionel Martin; Guillaume Cleuziou; Matthieu Exbrayat


Fouille de données complexes | 2011

Clustering multi-vues : une approche centralisée.

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

Clustering multi-vues : une approche centralisée

Jacques-Henri Sublemontier; Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin


Groupe de Travail EGC-FDC | 2010

Variante à noyaux pour la classification multi-vues non supervisée

Jacques-Henri Sublemontier; Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin


XVIèmes Rencontres de la Société Francophone de Classification | 2009

Classification non-supervisée de données multi-représentées par une approche collaborative.

Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin; Jacques-Henri Sublemontier


Groupe de Travail EGC-FDC | 2009

De l'integration de la collaboration au sein du processus de clustering pour le traitement de données multi­-représentées

Jacques-Henri Sublemontier; Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin


EGC 2009 | 2009

Regroupement de données multi-représentées : une approche par k-moyenne flou

Jacques-Henri Sublemontier; Guillaume Cleuziou; Matthieu Exbrayat; Lionel Martin

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