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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 "funIHC" in publications use:'
type: software
license: MIT
title: 'funIHC: Functional Iterative Hierarchical Clustering'
version: 0.1.0
doi: 10.1007/s11634-024-00611-8
identifiers:
- type: doi
  value: 10.32614/CRAN.package.funIHC
abstract: Functional clustering aims to group curves exhibiting similar temporal behaviour
  and to obtain representative curves summarising the typical dynamics within each
  cluster. A key challenge in this setting is class imbalance, where some clusters
  contain substantially more curves than others, which can adversely affect clustering
  performance. While class imbalance has been extensively studied in supervised classification,
  it has received comparatively little attention in unsupervised clustering. This
  package implements functional iterative hierarchical clustering ('funIHC'), an adaptation
  of the iterative hierarchical clustering method originally developed for multivariate
  data, to the functional data setting. For further details, please see Higgins and
  Carey (2024) <https://doi.org/10.1007/s11634-024-00611-8>.
authors:
- family-names: Higgins
  given-names: Catherine
  email: catherine.higgins@ucd.ie
- family-names: Carey
  given-names: Michelle
  email: michelle.carey@ucd.ie
preferred-citation:
  type: article
  title: Addressing class imbalance in functional data clustering
  authors:
  - family-names: Higgins
    given-names: Catherine
    email: catherine.higgins@ucd.ie
  - family-names: Carey
    given-names: Michelle
    email: michelle.carey@ucd.ie
  journal: Advances in Data Analysis and Classification
  year: '2024'
  doi: 10.1007/s11634-024-00611-8
repository: https://cran.r-universe.dev
commit: 7f6ccf76de6bf73d2989dd3fedc105c12fb81d84
date-released: '2026-01-09'
contact:
- family-names: Higgins
  given-names: Catherine
  email: catherine.higgins@ucd.ie