2026-04-14T08:51:03Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/447302025-12-18T01:49:22Zcom_2072_6col_2072_452953

Use of Correspondence Analysis in Clustering a Mixed-Scale Data Set with Missing Data Greenacre, Michael Universitat Pompeu Fabra. Departament d'Economia i Empresa Statistics, Econometrics and Quantitative Methods 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. 2020-05-25T09:26:57Z 2020-05-25T09:26:57Z 2019-01-01 2020-05-25T09:25:44Z info:eu-repo/semantics/workingPaper https://econ-papers.upf.edu/ca/paper.php?id=1626 http://hdl.handle.net/10230/44730 eng Economics and Business Working Papers Series; 1626 L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess application/pdf application/pdf