Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R

Autor: Angelos Markos, Alfonso Iodice D'Enza, Michel van de Velden

Publikováno v: Journal of Statistical Software, Vol 91, Iss 1, Pp 1-24 (2019)

We present the R package clustrd which implements a class of methods that combine dimension reduction and clustering of continuous or categorical data. In particular, for continuous data, the package contains implementations of factorial K-means and

Externí odkaz: https://doaj.org/article/02ade52c361c4ac6aacfedff1f301844

Multidimensional Scaling by Majorization: A Review

Autor: Patrick J. F. Groenen, Michel van de Velden

Publikováno v: Journal of Statistical Software, Vol 73, Iss 1, Pp 1-26 (2016)

A major breakthrough in the visualization of dissimilarities between pairs of objects was the formulation of the least-squares multidimensional scaling (MDS) model as defined by the Stress function. This function is quite flexible in that it allows p

Externí odkaz: https://doaj.org/article/485b3c9fa6824a8e86c867a4ae49b1f0

A general framework for implementing distances for categorical variables

Autor: Michel Van De Velden, Alfonso Iodice D’Enza, Angelos Markos, Carlo Cavicchia

The degree to which subjects differ from each other with respect to certain properties measured by a set of variables, plays an important role in many statistical methods. For example, classification, clustering, and data visualization methods all re

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ffbcf77e9654f895a42e3f2d8af16873
http://arxiv.org/abs/2301.02190

Retrieving a contingency table from a correspondence analysis solution

Autor: Michel van de Velden, Hugo Galy, Patrick J. F. Groenen, Wilco van den Heuvel

Publikováno v: European Journal of Operational Research, 283(2), 541-548. Elsevier

Correspondence analysis (CA) is a dimension reduction technique for categorical data. In particular, CA is typically applied to a contingency matrix in order to visualize the relationships within and between the categories of the two variables as rep

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a0bd980f4abd4b08a35fc5af7a9eb84
https://doi.org/10.1016/j.ejor.2019.11.014

Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R

Autor: Michel van de Velden, Alfonso Iodice D'Enza, Angelos Markos

Publikováno v: Journal of Statistical Software, Vol 91, Iss 1, Pp 1-24 (2019)
Journal of Statistical Software, 91(10). University of California at Los Angeles
Journal of Statistical Software; Vol 91 (2019); 1-24

We present the R package clustrd which implements a class of methods that combine dimension reduction and clustering of continuous or categorical data. In particular, for continuous data, the package contains implementations of factorial K-means and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7673aec7587bb235a663bf563e021234
https://www.jstatsoft.org/index.php/jss/article/view/2915