kamila: 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) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou (2018) <doi:10.18637/jss.v083.i13>.

Package details

AuthorAlexander Foss [aut, cre], Marianthi Markatou [aut]
MaintainerAlexander Foss <alexanderhfoss@gmail.com>
LicenseGPL-3 | file LICENSE
URL https://github.com/ahfoss/kamila
Package repositoryView on CRAN
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kamila documentation built on March 13, 2020, 9:08 a.m.