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 |
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Author | Alexander Foss [aut, cre], Marianthi Markatou [aut] |
Maintainer | Alexander Foss <alexanderhfoss@gmail.com> |
License | GPL-3 | file LICENSE |
Version | 0.1.2 |
URL | https://github.com/ahfoss/kamila |
Package repository | View on CRAN |
Installation |
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