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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "RelativeDistClust" in publications use:'
type: software
license: GPL-3.0-only
title: 'RelativeDistClust: Clustering with a Novel Non Euclidean Relative Distance'
version: 0.1.0
doi: 10.32614/CRAN.package.RelativeDistClust
abstract: 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) <https://doi.org/10.2307/2346830> and an explanation
  of the k-medoids algorithm can be found in Reynolds et al (2006) <https://doi.org/10.1007/s10852-005-9022-1>.
authors:
- family-names: Creus
  given-names: Irene
  name-suffix: Martí
  email: ircrmar@mat.upv.es
  orcid: https://orcid.org/0000-0002-7962-4478
repository: https://creus-marti.r-universe.dev
commit: 09779eaa93ae9690ff6969185b716fb4e8476b0c
date-released: '2025-09-22'
contact:
- family-names: Creus
  given-names: Irene
  name-suffix: Martí
  email: ircrmar@mat.upv.es
  orcid: https://orcid.org/0000-0002-7962-4478