Clustering with a Novel Non Euclidean Relative Distance
Overview
Using the novel Relative Distance to cluster datasets. Implementation of a clustering approach based on the k-means algorithm that can be used with any distance. In addition, implementation of the Hartigan and Wong method to accommodate alternative distance metrics. Both methods can operate with any distance measure, provided a suitable method is available to compute cluster centers under the chosen metric. Additionally, the k-medoids algorithm is implemented, offering a robust alternative for clustering without the need of computing cluster centers under the chosen metric. All three methods are designed to support Relative distances, Euclidean distances, and any user-defined distance functions. The Hartigan and Wong method is described in Hartigan and Wong (1979) doi:10.2307/2346830 and an explanation of the k-medoids algorithm can be found in Reynolds et al (2006) doi:10.1007/s10852-005-9022-1.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 33
- Examples that run
- 100%33 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 24%
Downloads
Dependencies
Installing RelativeDistClust adds 118 packages (59 with compiled code, 113 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, bslib 5.6 MB.
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2025-09-22 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-09-22
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Download size
- 33 KB
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