Exploiting Data Reliability and Fuzzy Clustering for Journal Ranking
- Submitting institution
-
Aberystwyth University / Prifysgol Aberystwyth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9709877
- Type
- D - Journal article
- DOI
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10.1109/TFUZZ.2016.2612265
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 1306
- Volume
- 25
- Issue
- 5
- ISSN
- 1063-6706
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- 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
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 22
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Presents a novel approach for approximate clustering based on fuzzy aggregation with innovative application to journal ranking. IEEE TFS is one of the top-rated outlets across all areas of computer science and informatics (JCR-Clarivate). Led to a good number of further developments, including research done by world-leading groups in the area (e.g., Federation University and University of South Australia, Australia; Nanjing University of Aeronautics and Astronautics, China; National Kaohsiung University of Science and Technology, Taiwan; California State University, USA).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -