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KQM  

K Quantiles Medoids (KQM) Clustering
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("KQM", "1.1.1")



Attach the package and use:
library("KQM")
Maintained by
Yarong Yang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-10-16
Latest Update: 2025-10-16
Description:
K Quantiles Medoids (KQM) clustering applies quantiles to divide data of each dimension into K mean intervals. Combining quantiles of all the dimensions of the data and fully permuting quantiles on each dimension is the strategy to determine a pool of candidate initial cluster centers. To find the best initial cluster centers from the pool of candidate initial cluster centers, two methods based on quantile strategy and PAM strategy respectively are proposed. During a clustering process, medoids of clusters are used to update cluster centers in each iteration. Comparison between KQM and the method of randomly selecting initial cluster centers shows that KQM is almost always getting clustering results with smaller total sum squares of distances.
How to cite:
Yarong Yang (2025). KQM: K Quantiles Medoids (KQM) Clustering. R package version 1.1.1, https://cran.r-project.org/web/packages/KQM. Accessed 24 Oct. 2026.
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