Books
Our books presented in the course section describe the main methods PCA, CA, MCA and clustering.
References: papers and bibliography
How to cite the FactoMineR package?
- Lê, S., Josse, J. & Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software. 25(1). pp. 1-18.
References on the FactoMineR package
- Lê, S., Josse, J. & Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software. 25(1). pp. 1-18.
- Kostov, B., Bécue-Bertaut, M., Husson, F. (2013). Multiple Factor Analysis for Contingency Tables in FactoMineR Package. R Journal, 5, 28-38.
- Kostov, B., Bécue-Bertaut, M., Husson F. (2015). Correspondence Analysis on Generalised Aggregated Lexical Tables (CA-GALT) in the FactoMineR Package. R Journal. vol. 7 num. 1. pp. 109-117
- Josse, J. & Husson, F. (2016). missMDA a package to handle missing values in principal component methods. Journal of Statistical Software, 70(1).
Other papers
on MFA
- Jérôme Pagès. Multiple Factor Analysis: main features and application to sensory data
- Marie de Tayrac, Sébastien Lê, Marc Aubry, François Husson, and Jean Mosser (2009).Integrating "omics" data sets and biological knowledge: Multiple Factor Analysis as a powerful strategy. BMC Genomics 2009, 10:32
on Hierarchical Clustering
- Husson, F., Josse, J. & Pagès J. (2010). Principal component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data?. Technical report.
Bibliography
Benzécri J.P. (1992). Correspondence Analysis Handbook. (Transl : T.K. Gopalan) Marcel Dekker, New York.
Escofier B. & Pagès J. (2008). Analyses Factorielles Simples et Multiples: Objectifs, Méthodes et Interprétation. Dunod, 4th edn, Paris.
Govaert, G. (2009). Data Analysis. Wiley-ISTE
Greenacre, M.J.. (2007). Correspondence analysis in practice. Chapman & Hall/CRC.
Greenacre, M.J. and Blasius, J. (2006). Multiple correspondence analysis and related methods. Chapman & Hall/CRC.
Greenacre M. (1984). Theory and applications of correspondence analysis. Acadamic Press.
Husson F., Lê S., Pagès J. (2017). Exploratory Multivariate Analysis by Example Using R. 2nd edition. Chapman & Hall/CRC.
Jolliffe I. (2002). Principal Component Analysis. Springer. 2nd edn.
Kaufman L. & Rousseeuw P. (1990). Finding groups in data. An introduction to cluster analysis. Wiley and sons, Inc. New-York.
Le Roux B. & Rouanet H. (2004). Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis. Dordrecht: Kluwer.
Le Roux B. & Rouanet H. (2004). Multiple Correspondence Analysis. SAGE, Series: Quantitative Applications in the Social Sciences.
Lebart L., Morineau A. & Warwick K. (1984). Multivariate descriptive statistical analysis. Wiley, New-York.
Mirkin B. (2005). Clustering For Data Mining: A Data Recovery Approac. Chapman & Hall/CRC.
Murtagh F. (1985). Multidimensional Clustering Algorithms. Vienna: Physica-Verlag, COMPSTAT Lectures.
Murtagh F. (2005). Correspondence Analysis and Data Coding with R and Java. Chapman & Hall/CRC.
Pagès J. (2015). Multiple Factor Analysis by Example Using R.. Chapman & Hall/CRC. (see more details here)
