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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