Multidimensional Scaling for Big Data

MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of three algorithms: - Divide and Conquer MDS developed by Delicado and Pachon-Garcia, (2020) . - Fast MDS, which is an implementation of Tynia, Y., L. Jinze, M. Leonard, and W. Wei, (2006). - MDS based on Gower interpolation, which uses Gower interpolation formula as described in Gower, J.C. and D.J, Hand (1995, ISBN: 978-0-412-71630-0). The main idea of these methods is based on partitioning the dataset into small pieces, where classical methods can work. In order to align all the solutions, it is used Procrustes formula as described in Borg, I. and Groenen, P. (2005, ISBN : 978-0-387-25150-9).


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install.packages("bigmds")

1.0.0 by Cristian Pachón García, 25 days ago


https://github.com/pachoning/bigmds


Report a bug at https://github.com/pachoning/bigmds/issues


Browse source code at https://github.com/cran/bigmds


Authors: Cristian Pachón García [aut, cre] , Pedro Delicado [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports stats, pdist

Suggests testthat


See at CRAN