 Multiple factor analysis and clustering of a mixture of quantitative, categorical and frequency dataMonica Bécue-Bertaut and Jérome Pagès Computational Statistics & Data Analysis, 2008, vol. 52, issue 6, 3255-3268 Abstract: Analysing and clustering units described by a mixture of sets of quantitative, categorical and frequency variables is a relevant challenge. Multiple factor analysis is extended to include these three types of variables in order to balance the influence of the different sets when a global distance between units is computed. Suitable coding is adopted to keep as close as possible to the approach offered by principal axes methods, that is, principal component analysis for quantitative sets, multiple correspondence analysis for categorical sets and correspondence analysis for frequency sets. In addition, the presence of frequency sets poses the problem of selecting the unit weighting, since this is fixed by the user (usually uniform) in principal component analysis and multiple correspondence analysis, but imposed by the table margin in correspondence analysis. The method's main steps are presented and illustrated by an example extracted from a survey that aimed to cluster respondents to a questionnaire that included both closed and open-ended questions. Date: 2008 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:52:y:2008:i:6:p:3255-3268 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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