Clustering of nonstationary data streams: A survey of fuzzy partitional methods
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 33
- Type
- D - Journal article
- DOI
-
10.1002/widm.1258
- Title of journal
- Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
- Article number
- e1258
- First page
- -
- Volume
- 8
- Issue
- 4
- ISSN
- 1942-4787
- Open access status
- Technical exception
- Month of publication
- -
- Year of publication
- 2018
- URL
-
https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1258
- 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)
-
-
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper, published in a leading journal on data mining and knowledge discovery, with high impact factor, presents some key discoveries in the problem of clustering non-stationary data streams. This paper discusses relevant data stream clustering algorithms focusing mainly on fuzzy methods, including their treatment of outliers and concept drift and shift. This is important for real-time management of large and complex systems like urban traffic or data networks. Such networks produce non-stationary data streams. Similar behaviour can be observed in energy grids were the energy flow is processed including the energy flow coming from renewable sources.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
