Methods for Joint Dimension Reduction and Clustering

A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; ). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; ) and reduced K-means (De Soete and Carroll 1994; ); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; ), i-FCB (Iodice D'Enza and Palumbo 2013, ) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; ), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; ), which combine PCA for mixed-type data with K-means.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.3.7-2 by Angelos Markos, a year ago

Browse source code at

Authors: Angelos Markos [aut, cre] , Alfonso Iodice D'Enza [aut] , Michel van de Velden [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports rARPACK, tibble, corpcor, GGally, fpc, cluster, dplyr, plyr, ggrepel, ca, stats

Depends on ggplot2, grid

Suggested by FCPS.

See at CRAN