Reproducible k-means clustering in galaxy feature data from the GAMA survey
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 994
- Type
- D - Journal article
- DOI
-
10.1093/mnras/sty2690
- Title of journal
- Monthly Notices of the Royal Astronomical Society
- Article number
- -
- First page
- 126
- Volume
- 482
- Issue
- 1
- ISSN
- 0035-8711
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
8
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper to use a reproducible framework to cluster astrophysical data with k-means. While this clustering method is widely used, its application is usually heuristic. In this work, a systematic procedure ensures that the most stable clusters are found, with the most informative cluster number. The method is generic and proposes best practice to obtain robust results when clustering with k-means. It is therefore generic in its scope. This particular instance is published in a well-established journal and the article reached an Altmetric score above 10 in the year of publication.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
