Visualizing Profiles of Large Datasets of Weighted and Mixed Data

Grané Chávez, Aurea; Sow-Barry, Alpha A.

Publication:
Visualizing Profiles of Large Datasets of Weighted and Mixed Data

dc.affiliation.dpto	UC3M. Departamento de Estadística	es
dc.contributor.author	Grané Chávez, Aurea
dc.contributor.author	Sow-Barry, Alpha A.
dc.contributor.funder	Ministerio de Economía y Competitividad (España)	es
dc.date.accessioned	2021-06-30T09:36:37Z
dc.date.available	2021-06-30T09:36:37Z
dc.date.issued	2021-04-02
dc.description.abstract	This work provides a procedure with which to construct and visualize profiles, i.e., groups of individuals with similar characteristics, for weighted and mixed data by combining two classical multivariate techniques, multidimensional scaling (MDS) and the k-prototypes clustering algorithm. The well-known drawback of classical MDS in large datasets is circumvented by selecting a small random sample of the dataset, whose individuals are clustered by means of an adapted version of the k-prototypes algorithm and mapped via classical MDS. Gower’s interpolation formula is used to project remaining individuals onto the previous configuration. In all the process, Gower’s distance is used to measure the proximity between individuals. The methodology is illustrated on a real dataset, obtained from the Survey of Health, Ageing and Retirement in Europe (SHARE), which was carried out in 19 countries and represents over 124 million aged individuals in Europe. The performance of the method was evaluated through a simulation study, whose results point out that the new proposal solves the high computational cost of the classical MDS with low error.	en
dc.description.sponsorship	This research was funded by the Spanish Ministry of Economy and Competitiveness, grant number MTM2014-56535-R; and the V Regional Plan for Scientific Research and Technological Innovation 2016-2020 of the Community of Madrid, an agreement with Universidad Carlos III de Madrid in the action of "Excellence for University Professors."	en
dc.format.extent	20
dc.identifier.bibliographicCitation	Grané, A. & Sow-Barry, A. A. (2021). Visualizing Profiles of Large Datasets of Weighted and Mixed Data. Mathematics, 9(8), 891.	en
dc.identifier.doi	https://doi.org/10.3390/math9080891
dc.identifier.issn	2227-7390
dc.identifier.publicationfirstpage	891
dc.identifier.publicationissue	8
dc.identifier.publicationtitle	Mathematics	en
dc.identifier.publicationvolume	9
dc.identifier.uri	https://hdl.handle.net/10016/32960
dc.identifier.uxxi	AR/0000027865
dc.language.iso	eng
dc.publisher	MDPI
dc.relation.projectID	Gobierno de España. MTM2014-56535-R	es
dc.rights	© 2021 by the authors.	en
dc.rights	Atribución 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/	*
dc.subject.eciencia	Estadística	es
dc.subject.other	Clustering	en
dc.subject.other	Gower's interpolation formula	en
dc.subject.other	Gower's metric	en
dc.subject.other	Mixed data	en
dc.subject.other	Multidimensional scaling	en
dc.title	Visualizing Profiles of Large Datasets of Weighted and Mixed Data	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication