Document clustering with evolved search queries
- Submitting institution
-
Sheffield Hallam University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1352
- Type
- E - Conference contribution
- DOI
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10.1109/CEC.2017.7969447
- Title of conference / published proceedings
- 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
- First page
- 1239
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2017
- 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
-
1
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The research was the first attempt to provide text document clustering using automatically generated search queries. Since publication work has continued; the fitness test has been enhanced and simplified and a new and more extensive study is ongoing in collaboration with Dr Teresa Brunsdon from Warwick university. Research at the Sri Lanka Institute of Information Technology has applied the clustering algorithm to new text datasets in the Sinhala language with promising results (Haddela, 2020, Springer). The program code for the clustering system has been made available as an open-source library.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -