Improving K-means clustering with enhanced Firefly Algorithms
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 25208583
- Type
- D - Journal article
- DOI
-
10.1016/j.asoc.2019.105763
- Title of journal
- Applied Soft Computing
- Article number
- 105763
- First page
- -
- Volume
- 84
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- 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
-
6
- Research group(s)
-
C - Digital Learning Laboratory (DLL)
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper features novel evolving hybrid clustering models; the work has led to one author being invited to be the guest editor for the journal Physiological Measurement. For a special issue on Remote Health Monitoring see https://iopscience.iop.org/journal/0967-3334/page/Remote_Health_Monitoring.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -