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distanceHD  

Distance Metrics for High-Dimensional Clustering
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("distanceHD", "1.2")



Attach the package and use:
library("distanceHD")
Maintained by
Jung Ae Lee
[Scholar Profile | Author Map]
First Published: 2025-01-30
Latest Update: 2025-01-31
Description:
We provide three distance metrics for measuring the separation between two clusters in high-dimensional spaces. The first metric is the centroid distance, which calculates the Euclidean distance between the centers of the two groups. The second is a ridge Mahalanobis distance, which incorporates a ridge correction constant, alpha, to ensure that the covariance matrix is invertible. The third metric is the maximal data piling distance, which computes the orthogonal distance between the affine spaces spanned by each class. These three distances are asymptotically interconnected and are applicable in tasks such as discrimination, clustering, and outlier detection in high-dimensional settings.
How to cite:
Jung Ae Lee (2025). distanceHD: Distance Metrics for High-Dimensional Clustering. R package version 1.2, https://cran.r-project.org/web/packages/distanceHD. Accessed 03 Apr. 2025.
Previous versions and publish date:
1.0 (2025-01-30 19:50)
Other packages that cited distanceHD R package
View distanceHD citation profile
Other R packages that distanceHD depends, imports, suggests or enhances
Complete documentation for distanceHD
Functions, R codes and Examples using the distanceHD R package
Full distanceHD package functions and examples
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