opticskxi: OPTICS K-Xi Density-Based Clustering
Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>.
| Version: |
1.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
ggplot2, magrittr, rlang |
| Suggests: |
amap, dbscan, cowplot, fastICA, fpc, ggrepel, grid, grDevices, gtable, knitr, parallel, plyr, reshape2, stats, testthat, text2vec, utils |
| Published: |
2024-12-09 |
| DOI: |
10.32614/CRAN.package.opticskxi |
| Author: |
Thomas Charlon 
[aut, cre] |
| Maintainer: |
Thomas Charlon <charlon at protonmail.com> |
| BugReports: |
https://gitlab.com/thomaschln/opticskxi/-/issues |
| License: |
GPL-3 |
| URL: |
https://gitlab.com/thomaschln/opticskxi |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
opticskxi results |
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