Proximity Curves for Potential-Based Clustering
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 16
- Type
- D - Journal article
- DOI
-
10.1007/s00357-019-09348-y
- Title of journal
- Journal of Classification
- Article number
- 0
- First page
- -
- Volume
- 0
- Issue
- 0
- ISSN
- 0176-4268
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
https://link.springer.com/article/10.1007/s00357-019-09348-y
- 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
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This scholar publication introduces the new concept of proximity curve and subsequent algorithms for data clustering that open new ways of processing data based on data intrinsic properties. Each data instance is described and analysed by its potential that contributes to the identification of their proximity as clusters. This paper opens new ways to analyse data for both data analytics and machine learning communities, by introducing a new stream of clustering algorithms and concepts on data properties. The paper is viewed at source many (760) times, was presented in invited seminars, and referred for data analytics knowledge transfer projects.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
