Frédéric Flouvat

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The discovery of frequent patterns is a famous problem in data mining. While plenty of algorithms have been proposed during the last decade, only a few contributions have tried to understand the influence of datasets on the algorithms behavior. Being able to explain why certain algorithms are likely to perform very well or very poorly on some datasets is(More)
In this paper, we present an ongoing work to discover maximal frequent itemsets in a transactional database. We propose an algorithm called ABS for Adaptive Borders Search, which is in the spirit of algorithms based on the concept of dualization. From an abstract point of view, our contribution can be seen as an improvement of the basic APRIORI algorithm(More)
In the last decade, many data mining tools have been developed. They address most of the classical data mining problems such as classification, clustering or pattern mining. However, providing classical solutions for classical problems is not always sufficient. This is especially true for pattern mining problems known to be " repre-sentable as set " , an(More)
Health risks management such as epidemics study produces large quantity of spatio-temporal data. The development of new methods able to manage such specific characteristics becomes crucial. To tackle this problem, we define a theoretical framework for extracting spatio-temporal patterns (sequences representing evolution of locations and their neighborhoods(More)
Dans cet article, nous montrons comment les techniques de fouilles de données peuvent résoudre efficacement le problème de la réécriture de requêtes en termes de vues en présence de contraintes de valeurs. A partir d'une forma-lisation du problème de la réécriture dans le cadre de la logique de description ALN (O v), nous montrons comment ce problème se(More)
In this paper, we investigate the problem of query rewriting using views in a hybrid language allowing nominals (i.e., individual names) to occur in intentional descriptions. Of particular interest, restricted form of nominals where individual names refer to simple values enable the specification of value constraints, i.e, sets of allowed values for(More)
The discovery of frequent patterns is a famous problem in data mining. While plenty of algorithms have been proposed during the last decade, only a few contributions have tried to understand the influence of datasets on the algorithms behavior. Being able to explain why certain algorithms are likely to perform very well or very poorly on some datasets is(More)
Extraction of interesting colocations in geo-referenced data is one of the major tasks in spatial pattern mining. The goal is to find sets of spatial object-types with instances located in the same neighborhood. In this context, the main drawback is the visualization and interpretation of extracted patterns by domain experts. Indeed, common textual(More)
Directed acyclic graphs can be used across many application domains. In this paper, we study a new pattern domain for supporting their analysis. Therefore, we propose the pattern language of weighted paths, primitive constraints that enable to specify their relevancy (e.g., frequency and com-pactness constraints), and algorithms that can compute the(More)