An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach
- Submitting institution
-
The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 182620420
- Type
- D - Journal article
- DOI
-
10.1007/s10115-016-0930-3
- Title of journal
- Knowledge and Information Systems
- Article number
- -
- First page
- 27
- Volume
- 50
- Issue
- 1
- ISSN
- 0219-1377
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2017
- 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
-
1
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Heterogeneous data is emerging in many areas as citations to the paper show. Our application to the medical domain enabled efficient clustering of prostate cancer pathways defined by text reports, structured data and biochemical results over time. A project on Prostate Cancer Pathways is now being driven by the Norwich & Norfolk University Hospital with our input. Currently the algorithm is being applied to cluster multi-time series atmospheric pollution data, linked to a PhD student. A grant proposal with Leo Alexandre(MED) expected in 2021 will apply such algorithms to missed oesophageal cancers by analysing medical reports and images.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
