Fully online clustering of evolving data streams into arbitrarily shaped clusters
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 250318807
- Type
- D - Journal article
- DOI
-
10.1016/j.ins.2016.12.004
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 96
- Volume
- 382-383
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2016
- 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
-
2
- Research group(s)
-
B - Data Science
- Citation count
- 42
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Traditional clustering methods form clusters with pre-defined shape which is defined by the type of distance metric used. There are very few methods that allow clusters with arbitrary shape to be formed. This paper offers a new fully online clustering method for data streams which can form clusters with arbitrary shape which, unlike others, does not require user- or problem-specific thresholds and parameters to be defined. The paper is published in the very prestigious Information Science journal and attracted a lot of citations. The work is part of £3M NERC-funded project involving NASA with high impact on climate research.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
