Methods for Clustering Mixed-Type Data

Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) and Foss & Markatou (2018) .


Reference manual

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0.1.2 by Alexander Foss, 2 years ago

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Authors: Alexander Foss [aut, cre] , Marianthi Markatou [aut]

Documentation:   PDF Manual  

GPL-3 | file LICENSE license

Imports stats, abind, KernSmooth, gtools, Rcpp, plyr

Suggests testthat, clustMD, ggplot2, Hmisc

Linking to Rcpp

See at CRAN