Novel centroid selection approaches for KMeans-clustering based recommender systems
- Submitting institution
-
University of East London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11
- Type
- D - Journal article
- DOI
-
10.1016/j.ins.2015.03.062
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 156
- Volume
- 320
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- 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
-
5
- Research group(s)
-
1 - Intelligent Systems
- Citation count
- 82
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper addresses scalability and accuracy issues associated with traditional recommender systems using k-means clustering algorithms. The impact of this work is significant, as k-means is a widely used algorithm. This work represents the outcome of international collaboration between UEL, University of Southampton UK, and UET Pakistan. Part of this work is being employed by two PhD theses from UET-Taxila Pakistan and Victoria University of Wellington, New Zealand in designing novel clustering algorithms for recommender systems. A collaborative work between the NHS and UEL is using this work to cluster medical data of cancer patients.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
