219 citations · 1.3k across the 47 of their papers we have counts for
18 papers · 1 filter
Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL
Marco Corneli, Pierre Latouche, Fabrice Rossi
We develop a model in which interactions between nodes of a dynamic network are counted by non homogeneous Poisson processes. In a block modelling perspective, nodes belong to hidd…
Discovering Patterns in Time-Varying Graphs: A Triclustering Approach
Romain Guigourès, Marc Boullé, Fabrice Rossi
This paper introduces a novel technique to track structures in time varying graphs. The method uses a maximum a posteriori approach for adjusting a three-dimensional co-clustering…
Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks
Marco Corneli, Pierre Latouche, Fabrice Rossi
The stochastic block model (SBM) is a flexible probabilistic tool that can be used to model interactions between clusters of nodes in a network. However, it does not account for in…
Co-Clustering Network-Constrained Trajectory Data
Mohamed Khalil El Mahrsi, Romain Guigourès, Fabrice Rossi +1
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and exte…
A Study of the Spatio-Temporal Correlations in Mobile Calls Networks
Romain Guigourès, Marc Boullé, Fabrice Rossi
For the last few years, the amount of data has significantly increased in the companies. It is the reason why data analysis methods have to evolve to meet new demands. In this arti…
Modelling time evolving interactions in networks through a non stationary extension of stochastic block models
Marco Corneli, Pierre Latouche, Fabrice Rossi
In this paper, we focus on the stochastic block model (SBM),a probabilistic tool describing interactions between nodes of a network using latent clusters. The SBM assumes that the…
Graphs in machine learning: an introduction
Pierre Latouche, Fabrice Rossi
Graphs are commonly used to characterise interactions between objects of interest. Because they are based on a straightforward formalism, they are used in many scientific fields fr…
Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks
Marco Corneli, Pierre Latouche, Fabrice Rossi
The latent block model (LBM) is a flexible probabilistic tool to describe interactions between node sets in bipartite networks, but it does not account for interactions of time var…
Nonparametric Hierarchical Clustering of Functional Data
Marc Boullé, Romain Guigourès, Fabrice Rossi
In this paper, we deal with the problem of curves clustering. We propose a nonparametric method which partitions the curves into clusters and discretizes the dimensions of the curv…
How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?
Fabrice Rossi
In numerous applicative contexts, data are too rich and too complex to be represented by numerical vectors. A general approach to extend machine learning and data mining techniques…
Regularization in Relevance Learning Vector Quantization Using l one Norms
Martin Riedel, Marika Kästner, Fabrice Rossi +1
We propose in this contribution a method for l one regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance pr…
A bag-of-paths framework for network data analysis
Kevin Françoisse, Ilkka Kivimäki, Amin Mantrach +2
This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of al…
Modularity-Based Clustering for Network-Constrained Trajectories
Mohamed Khalil El Mahrsi, Fabrice Rossi
We present a novel clustering approach for moving object trajectories that are constrained by an underlying road network. The approach builds a similarity graph based on these traj…
Dissimilarity Clustering by Hierarchical Multi-Level Refinement
Brieuc Conan-Guez, Fabrice Rossi
We introduce in this paper a new way of optimizing the natural extension of the quantization error using in k-means clustering to dissimilarity data. The proposed method is based o…
Clustering Dynamic Web Usage Data
Alzennyr Da Silva, Yves Lechevallier, Fabrice Rossi +1
Most classification methods are based on the assumption that data conforms to a stationary distribution. The machine learning domain currently suffers from a lack of classification…
Constrained variable clustering and the best basis problem in functional data analysis
Fabrice Rossi, Yves Lechevallier
Functional data analysis involves data described by regular functions rather than by a finite number of real valued variables. While some robust data analysis methods can be applie…
Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach
Fabrice Rossi, Nathalie Villa-Vialaneix
This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produce…
Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation
Georges Hébrail, Bernard Hugueney, Yves Lechevallier +1
We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into clusters and represents each cluster by a simple p…