Comparer des méthodes

	Analyse des Données Symboliques	Analyse des données compositionnelles (CoDA)
Domaine	Soft computing	Statistique
Famille	Machine learning	Regression model
Année d'origine	2003	1982
Auteur d'origine	Edwin Diday; Lynne Billard	John Aitchison
Type	Statistical framework for aggregate and set-valued data	Constrained multivariate statistical method
Source fondatrice	Billard, L., & Diday, E. (2003). From the statistics of data to the statistics of knowledge: symbolic data analysis. Journal of the American Statistical Association, 98(462), 470–487.	Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B, 44(2), 139–177.
Alias	SDA, Interval Data Analysis, Distributional Data Analysis, Sembolik Veri Analizi	CoDA, Simplex Analysis, Log-Ratio Analysis, Bileşim Veri Analizi
Apparentées	1	2
Résumé	Symbolic Data Analysis (SDA) is a statistical framework designed to analyze complex, aggregate, or set-valued data — called symbolic data — in which each observation represents a group or concept rather than a single scalar. Introduced in its modern statistical form by Lynne Billard and Edwin Diday in 2003, SDA extends classical statistics to handle interval-valued, histogram-valued, and multi-valued variables, enabling rigorous inference at the level of knowledge rather than raw individual records.	Compositional Data Analysis (CoDA) is a branch of multivariate statistics designed for data that represent parts of a whole — proportions, percentages, or concentrations that sum to a constant. Introduced by John Aitchison in his landmark 1982 paper, CoDA recognises that standard Euclidean methods fail on the simplex and instead operates through log-ratio transformations that respect the relative nature of compositional information.