Crandore Hub

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>.

Versions across snapshots

VersionRepositoryFileSize
0.1.2 rolling linux/jammy R-4.5 kamila_0.1.2.tar.gz 147.6 KiB
0.1.2 rolling linux/noble R-4.5 kamila_0.1.2.tar.gz 148.7 KiB
0.1.2 rolling source/ R- kamila_0.1.2.tar.gz 43.3 KiB
0.1.2 latest linux/jammy R-4.5 kamila_0.1.2.tar.gz 147.6 KiB
0.1.2 latest linux/noble R-4.5 kamila_0.1.2.tar.gz 148.7 KiB
0.1.2 latest source/ R- kamila_0.1.2.tar.gz 43.3 KiB
0.1.2 2026-04-26 source/ R- kamila_0.1.2.tar.gz 43.3 KiB
0.1.2 2026-04-23 source/ R- kamila_0.1.2.tar.gz 43.3 KiB
0.1.2 2026-04-09 windows/windows R-4.5 kamila_0.1.2.zip 465.6 KiB
0.1.2 2025-04-20 source/ R- kamila_0.1.2.tar.gz 43.3 KiB

Dependencies (latest)

Imports

LinkingTo

Suggests