Lattice-based biclustering using Partition Pattern Structures

International audienceIn this work we present a novel technique for exhaustive bicluster enumeration using formal concept anal-ysis (FCA). Particularly, we use pattern structures (an ex-tension of FCA dealing with complex data) to mine similar row/column biclusters, a specialization of biclustering when attribute values have coherent variations. We show how bi-clustering can benefit from the FCA framework through its ro-bust theoretical description and efficient algorithms. Finally, we evaluate our bicluster mining approach w.r.t. a standard biclustering technique showing very good results in terms of bicluster quality and performance

Application of Biclustering to the Discovery of Constant and Gradual Patterns

International audienc

Biclustering Based on FCA and Partition Pattern Structures for Recommendation Systems

National audienceThis paper focuses on item recommendation for visitors in a museum within the framework of European Project CrossCult about cultural heritage. We present a theoretical research work about recommendation using bicluster-ing. Our approach is based on biclustering using FCA and partition pattern structures. We investigate the possibility of incorporating the order information using this approach. Then, given the dataset of visitor trajectories, the result of our biclustering can be used to build a collaborative recommendation system

Application des Pattern Structures à la découverte de biclusters à changements de signes cohérents

National audienceLe "biclustering" joue un rôle majeur dans beaucoup d'applications du monde réel. Il est lié au "clustering" qui regroupe des lignes similaires dans une matrice de données numériques, tandis que le biclustering cherche à re-grouper simultanément des lignes et colonnes similaires, c'est-à-dire trouver des sous-matrices où émerge une corrélation entre les entrées. Le biclustering s'ap-puie sur un critère de similarité, et dans cet article, nous nous intéressons au biclustering "à colonnes constantes" (CC), où les valeurs numériques dans les colonnes des sous-matrices sont constantes pour chaque ligne. L'étude est en-suite étendue au biclustering à "changements de signes cohérents" (CSC), où la différence entre les valeurs de deux colonnes consécutives de la sous-matrice est du même signe pour chaque ligne

Characterizing Covers of Functional Dependencies using FCA

International audienceFunctional dependencies (FDs) can be used for various important operations on data, for instance, checking the consistency and the quality of a database (including databases that contain complex data). Consequently, a generic framework that allows mining a sound, complete, non-redundant and yet compact set of FDs is an important tool for many different applications. There are different definitions of such sets of FDs (usually called cover). In this paper, we present the characterization of two different kinds of covers for FDs in terms of pattern structures. The convenience of such a characterization is that it allows an easy implementation of efficient mining algorithms which can later be easily adapted to other kinds of similar dependencies. Finally, we present empirical evidence that the proposed approach can perform better than state-of-the-art FD miner algorithms in large databases

Eighth International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2020)

International audienceProceedings of the 8th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2020)co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, August 29, 202