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briKmeans  

Package for Brik, Fabrik and Fdebrik Algorithms to Initialise Kmeans
View on CRAN: Click here


Download and install briKmeans package within the R console
Install from CRAN:
install.packages("briKmeans")

Install from Github:
library("remotes")
install_github("cran/briKmeans")

Install by package version:
library("remotes")
install_version("briKmeans", "1.0")



Attach the package and use:
library("briKmeans")
Maintained by
Aurora Torrente
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-15
Latest Update: 2022-07-21
Description:
Implementation of the BRIk, FABRIk and FDEBRIk algorithms to initialise k-means. These methods are intended for the clustering of multivariate and functional data, respectively. They make use of the Modified Band Depth and bootstrap to identify appropriate initial seeds for k-means, which are proven to be better options than many techniques in the literature. Torrente and Romo (2021) It makes use of the functions kma and kma.similarity, from the archived package fdakma, by Alice Parodi et al.
How to cite:
Aurora Torrente (2021). briKmeans: Package for Brik, Fabrik and Fdebrik Algorithms to Initialise Kmeans. R package version 1.0, https://cran.r-project.org/web/packages/briKmeans. Accessed 07 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:23), 0.1 (2021-02-15 10:40)
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Complete documentation for briKmeans
Functions, R codes and Examples using the briKmeans R package
Some associated functions: brik . elbowRule . fabrik . fdebrik . kma . kma.similarity . plotKmeansClustering . 
Some associated R codes: brik.R . elbowRule.R . fabrik.R . fdebrik.R . kma.R . kma.similarity.R . plotKmeansClustering.R .  Full briKmeans package functions and examples
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