Mining range associations for classification and characterization
- Submitting institution
-
Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 96067914
- Type
- D - Journal article
- DOI
-
10.1016/j.datak.2018.10.001
- Title of journal
- Data and Knowledge Engineering
- Article number
- -
- First page
- 92
- Volume
- 118
- Issue
- -
- ISSN
- 0169-023X
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- URL
-
https://doi.org/10.1016/j.datak.2018.10.001
- 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)
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A - Artificial intelligence and data analytics
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper explores mining a large quantity of numerical data and producing human interpretable results. The technique developed from this study and reported in this paper is uniquely able to derive numerical intervals from the data directly and dynamically, rather than using discretisation as a pre-processing step. Evaluation is performed on a range of popular benchmark datasets from the UCI Machine Learning Repository.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -