Somoclu: An Efficient Parallel Library for Self-Organizing Maps
- Submitting institution
-
Bangor University / Prifysgol Bangor
- Unit of assessment
- 12 - Engineering
- Output identifier
- UoA12_39
- Type
- D - Journal article
- DOI
-
10.18637/jss.v078.i09
- Title of journal
- Journal of Statistical Software
- Article number
- -
- First page
- 1
- Volume
- 78
- Issue
- 9
- ISSN
- 1548-7660
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v078i09/somoclu-1.7.4.tar.gz
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The strength of this self-organising map algorithm is that it works effectively both for interactive analysis as well as batch processing of massive data. Because of this, it is routinely used for analysing genome data at University of California (Berkeley) and Lawrence Berkeley National Laboratory (mBio 9, e00441 (2018); Scientific Reports 7, 40101 (2017)). It has established itself as a benchmark case which newly published algorithms are compared with. The accompanying open-source code has been downloaded more than 400 times; https://www.jstatsoft.org/article/view/v078i09
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
