A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model
Isabella Morlini ()
Advances in Data Analysis and Classification, 2012, vol. 6, issue 1, 5-28
Keywords: Clustering; E-government; Information and communication technologies; Latent variables; Mixed mode data; Scores estimate; 62 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://hdl.handle.net/10.1007/s11634-011-0101-z (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:6:y:2012:i:1:p:5-28
Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2
DOI: 10.1007/s11634-011-0101-z
Access Statistics for this article
Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs
More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().