# [AB14] Correspondence Analysis

**Chapitres de Livre : **
Titre du livre: "

*Encyclopedia of Social Networks and Mining*",
September 2014,
Springer,

pp. ..,
(

isbn: 978-1461461692)

**Mots clés: ** correspondence analysis, principal component analysis, PCA, data analysis, Data Mining, analyse des données

**Résumé: **
Correspondence analysis (CA) is an extension of principal component analysis (PCA) tailored to handle nominal
variables. Originally, CA was developed to analyze contingency tables in which a sample of observations is described by two nominal variables, but it was rapidly extended to the analysis of any data matrices with non-negative entries. The origin of CA can be traced to early work of Pearson or Fisher, but the modern version of correspondence analysis and its geometric interpretation comes from the 1960s in France
and is associated with the French school of data analysis (analyse des donnees) and was developed under the leadership of Jean-Paul Benzecri. As a technique, it was often discovered (and re-discovered) and so variations of CA can be found under several different names such as dual-scaling, optimal scaling, homogeneity analysis, or
reciprocal averaging, The multiple identities of correspondence analysis are a consequence of its large number of properties: Correspondence analysis can be defined as an optimal solution for a lot of apparently different problems.