20 citations · 52 across the 12 of their papers we have counts for
12 papers
Fast and fully-automated histograms for large-scale data sets
Valentina Zelaya Mendizábal, Marc Boullé, Fabrice Rossi
G-Enum histograms are a new fast and fully automated method for irregular histogram construction. By framing histogram construction as a density estimation problem and its automati…
Co-clustering based exploratory analysis of mixed-type data tables
Aichetou Bouchareb, Marc Boullé, Fabrice Clérot +1
Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as h…
Model Based Co-clustering of Mixed Numerical and Binary Data
Aichetou Bouchareb, Marc Boullé, Fabrice Clérot +1
Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix. Many approaches have been studied and have sh…
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…
Revisiting enumerative two-part crude MDL for Bernoulli and multinomial distributions (Extended version)
Marc Boullé, Fabrice Clérot, Carine Hue
We leverage the Minimum Description Length (MDL) principle as a model selection technique for Bernoulli distributions and compare several types of MDL codes. We first present a sim…
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…
Universal Approximation of Edge Density in Large Graphs
Marc Boullé
In this paper, we present a novel way to summarize the structure of large graphs, based on non-parametric estimation of edge density in directed multigraphs. Following coclustering…
Cats & Co: Categorical Time Series Coclustering
Dominique Gay, Romain Guigourès, Marc Boullé +1
We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid model…
Country-scale Exploratory Analysis of Call Detail Records through the Lens of Data Grid Models
Romain Guigourès, Dominique Gay, Marc Boullé +2
Call Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network:…
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…
A Triclustering Approach for Time Evolving Graphs
Romain Guigourès, Marc Boullé, Fabrice Rossi
This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the sou…