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>. A short video tutorial can be found at <https://www.youtube.com/watch?v=P2XAjqI5Lc4/>.
| Version: | 1.2.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | ggplot2,magrittr,Matrix,rlang |
| Suggests: | amap,dbscan,cowplot,fastICA,fpc,ggrepel, grid, grDevices,gtable,knitr, parallel,plyr,reshape2,testthat |
| Published: | 2025-03-09 |
| 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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