---
_id: '4050'
article_number: '110030'
author:
- first_name: Nico
  full_name: Migenda, Nico
  id: '218473'
  last_name: Migenda
  orcid: 0000-0002-7223-1735
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-7223-1735/work/177074489
- first_name: Ralf
  full_name: Möller, Ralf
  last_name: Möller
- first_name: Wolfram
  full_name: Schenck, Wolfram
  id: '224375'
  last_name: Schenck
  orcid: 0000-0003-3300-2048
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0003-3300-2048/work/177074491
citation:
  alphadin: '<span style="font-variant:small-caps;">Migenda, Nico</span> ; <span style="font-variant:small-caps;">Möller,
    Ralf</span> ; <span style="font-variant:small-caps;">Schenck, Wolfram</span>:
    Adaptive local Principal Component Analysis improves the clustering of high-dimensional
    data. In: <i>Pattern Recognition</i> Bd. 146, Elsevier BV (2024)'
  ama: Migenda N, Möller R, Schenck W. Adaptive local Principal Component Analysis
    improves the clustering of high-dimensional data. <i>Pattern Recognition</i>.
    2024;146. doi:<a href="https://doi.org/10.1016/j.patcog.2023.110030">10.1016/j.patcog.2023.110030</a>
  apa: Migenda, N., Möller, R., &#38; Schenck, W. (2024). Adaptive local Principal
    Component Analysis improves the clustering of high-dimensional data. <i>Pattern
    Recognition</i>, <i>146</i>. <a href="https://doi.org/10.1016/j.patcog.2023.110030">https://doi.org/10.1016/j.patcog.2023.110030</a>
  bibtex: '@article{Migenda_Möller_Schenck_2024, title={Adaptive local Principal Component
    Analysis improves the clustering of high-dimensional data}, volume={146}, DOI={<a
    href="https://doi.org/10.1016/j.patcog.2023.110030">10.1016/j.patcog.2023.110030</a>},
    number={110030}, journal={Pattern Recognition}, publisher={Elsevier BV}, author={Migenda,
    Nico and Möller, Ralf and Schenck, Wolfram}, year={2024} }'
  chicago: Migenda, Nico, Ralf Möller, and Wolfram Schenck. “Adaptive Local Principal
    Component Analysis Improves the Clustering of High-Dimensional Data.” <i>Pattern
    Recognition</i> 146 (2024). <a href="https://doi.org/10.1016/j.patcog.2023.110030">https://doi.org/10.1016/j.patcog.2023.110030</a>.
  ieee: N. Migenda, R. Möller, and W. Schenck, “Adaptive local Principal Component
    Analysis improves the clustering of high-dimensional data,” <i>Pattern Recognition</i>,
    vol. 146, 2024.
  mla: Migenda, Nico, et al. “Adaptive Local Principal Component Analysis Improves
    the Clustering of High-Dimensional Data.” <i>Pattern Recognition</i>, vol. 146,
    110030, Elsevier BV, 2024, doi:<a href="https://doi.org/10.1016/j.patcog.2023.110030">10.1016/j.patcog.2023.110030</a>.
  short: N. Migenda, R. Möller, W. Schenck, Pattern Recognition 146 (2024).
date_created: 2023-12-18T23:37:19Z
date_updated: 2026-03-17T15:28:54Z
department:
- _id: '103'
doi: 10.1016/j.patcog.2023.110030
funded_apc: '1'
intvolume: '       146'
language:
- iso: eng
project:
- _id: beb248c8-cd75-11ed-b77c-e432b4711f7b
  name: Institut für Systemdynamik und Mechatronik
publication: Pattern Recognition
publication_identifier:
  issn:
  - '00313203'
publication_status: published
publisher: Elsevier BV
quality_controlled: '1'
status: public
title: Adaptive local Principal Component Analysis improves the clustering of high-dimensional
  data
type: journal_article
user_id: '224375'
volume: 146
year: '2024'
...