Symbolic Data Analysis and Formal Concept Analysis

National audienceFormal concept analysis (FCA) can be used for designing concept lattices from binary data for knowledge discovery purposes. Pattern structures in FCA are able to deal with complex data. In addition, this formalism provides a concise and an efficient algorithmic view of the formalism of symbolic data analysis (SDA)L'analyse formelle de concepts (FCA) est utilisée pour construire des treillis de concepts à partir de tables de données binaires pour des besoins de découverte de connaissances. Les structures de patrons en FCA sont capables de prendre en compte des données complexes et de plus fournissent une vue concise et algorithmique efficace sur le formalisme des objets symboliques (SDA)

Homogénéité dans l'analyse conceptuelle : un cadre commun pour variables numériques, ordinales et modales

National audienceLe cadre de ce travail est l'analyse de données par les treillis de Galois. Les données peuvent avoir des valeurs ordonnées, intervalles ou prendre la forme de distribution de probabilités/fréquences. Elles sont traitées dans un cadre commun par un opérateur de généralisation calculant les intensions par intervalles. Pour les données de distribution, les concepts sont plus homogènes et plus facilement interprétables que ceux obtenus précedemment

Homogeneity and stability in conceptual analysis

International audienceThis work comes within the field of data analysis using Galois lattices. We consider ordinal, numerical single or interval data as well as data that consist on frequency/probability distributions on a finite set of categories. Data are represented and dealt with on a common framework, by defining a generalization operator that determines intents by intervals. In the case of distribution data, the obtained concepts are more homogeneous and more easily interpretable than those obtained by using the maximum and minimum operators previously proposed. The number of obtained concepts being often rather large, and to limit the influence of atypical elements, we propose to identify stable concepts using interval distances in a cross validation-like approach

Structuring Probabilistic Data by Galois Lattices

International audienceIn this paper we address the problem of organising probabilistic data by Galois concept lattices. Two lattices are proposed, the union lattice and the intersection lattice, corresponding to two distinct semantics, by choosing accordingly the join and meet operators. A new algorithm is proposed to construct the concept lattice. Two real data examples illustrate the presented approach

Minimum Tree Cost Quartet Puzzling

Quartet method, Distance method, Phylogenetic tree reconstruction,