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kdml  

Kernel Distance Metric Learning for Mixed-Type Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("kdml", "1.1.0")



Attach the package and use:
library("kdml")
Maintained by
John R. J. Thompson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-08-27
Latest Update: 2024-08-27
Description:
Distance metrics for mixed-type data consisting of continuous, nominal, and ordinal variables. This methodology uses additive and product kernels to calculate similarity functions and metrics, and selects variables relevant to the underlying distance through bandwidth selection via maximum similarity cross-validation. These methods can be used in any distance-based algorithm, such as distance-based clustering. For further details, we refer the reader to Ghashti and Thompson (2024) <<doi:10.48550/arXiv.2306.01890>> for dkps() methodology, and Ghashti (2024) <doi:10.14288/1.0443975> for dkss() methodology.
How to cite:
John R. J. Thompson (2024). kdml: Kernel Distance Metric Learning for Mixed-Type Data. R package version 1.1.0, https://cran.r-project.org/web/packages/kdml. Accessed 07 Nov. 2024.
Previous versions and publish date:
1.0.0 (2024-08-27 13:30)
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