A novel artificial bee colony based clustering algorithm for categorical data
- Submitting institution
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Heriot-Watt University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 29184752
- Type
- D - Journal article
- DOI
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10.1371/journal.pone.0127125
- Title of journal
- PLoS ONE
- Article number
- e0127125
- First page
- -
- Volume
- 10
- Issue
- 5
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2015
- URL
-
-
- Supplementary information
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-
- 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
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4
- Research group(s)
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-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first attempt to tackle categorical data clustering within the artificial bee colony framework, and very promising results have been reported. The research exemplifies the application of bio-inspired computing in data mining and inspires follow-up research in swarm-inspired data clustering. The research involved long-term collaborations with two top Chinese universities and led to four subsequent publications. It is supported by and contributed to the ESRC social media project (ES/M001628/1, £0.54M) involving clustering Twitter data. This paper also led to the SICSA PECE funding, which enables further collaboration with China.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -