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license: GPL-3.0-only
title: 'opticskxi: OPTICS K-Xi Density-Based Clustering'
version: 1.2.2
doi: 10.32614/CRAN.package.opticskxi
abstract: 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) <https://doi.org/10.13097/archive-ouverte/unige:161795>.
  A short video tutorial can be found at <https://www.youtube.com/watch?v=P2XAjqI5Lc4/>.
authors:
- family-names: Charlon
  given-names: Thomas
  email: charlon@protonmail.com
  orcid: https://orcid.org/0000-0001-7497-0470
repository: https://cran.r-universe.dev
repository-code: https://gitlab.com/thomaschln/opticskxi
commit: 72964e3bc9eb8f3d636f42d14f1e4c5cc9e3a64a
url: https://gitlab.com/thomaschln/opticskxi
date-released: '2026-06-10'
contact:
- family-names: Charlon
  given-names: Thomas
  email: charlon@protonmail.com
  orcid: https://orcid.org/0000-0001-7497-0470