Use of Correspondence Analysis in Clustering a Mixed-Scale Data Set with Missing Data
Full item page Simple item page
- dc.contributor.author Greenacre, Michael
- dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
- dc.date.accessioned 2020-05-25T09:26:57Z
- dc.date.available 2020-05-25T09:26:57Z
- dc.date.issued 2019-01-01
- dc.date.modified 2020-05-25T09:25:44Z
- dc.description.abstract Correspondence analysis is a method of dimension reduction for categorical data, providing many tools that can handle complex data sets. Observations on different measurement scales can be coded to be analysed together and missing data can also be handled in the categorical framework. In this study, the method s ability to cope with these problematic issues is illustrated, showing how a valid continuous sample space for a cluster analysis can be constructed from the complex data set from the IFCS 2017 Cluster Challenge.
- dc.format.mimetype application/pdf*
- dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=1626
- dc.identifier.citation
- dc.identifier.uri http://hdl.handle.net/10230/44730
- dc.language.iso eng
- dc.relation.ispartofseries Economics and Business Working Papers Series; 1626
- dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
- dc.subject.keyword
- dc.subject.keyword Statistics, Econometrics and Quantitative Methods
- dc.title Use of Correspondence Analysis in Clustering a Mixed-Scale Data Set with Missing Data
- dc.title.alternative
- dc.type info:eu-repo/semantics/workingPaper