Comparer des méthodes

	Analyse des données compositionnelles (CoDA)	Analyse des Données Symboliques
Domaine	Statistique	Soft computing
Famille	Regression model	Machine learning
Année d'origine	1982	2003
Auteur d'origine	John Aitchison	Edwin Diday; Lynne Billard
Type	Constrained multivariate statistical method	Statistical framework for aggregate and set-valued data
Source fondatrice	Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B, 44(2), 139–177.	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.
Alias	CoDA, Simplex Analysis, Log-Ratio Analysis, Bileşim Veri Analizi	SDA, Interval Data Analysis, Distributional Data Analysis, Sembolik Veri Analizi
Apparentées	2	1
Résumé	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.	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.