kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data
- Submitting institution
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University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1324157
- Type
- D - Journal article
- DOI
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10.1016/j.knosys.2016.06.012
- Title of journal
- Knowledge-Based Systems
- Article number
- -
- First page
- 3
- Volume
- 117
- Issue
- -
- ISSN
- 0950-7051
- Open access status
- Compliant
- Month of publication
- June
- 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
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3
- Research group(s)
-
-
- Citation count
- 95
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This highly cited paper, in the top 1% in Computer Science, provides a big data design to enable one of the most influential data mining techniques, the nearest neighbour classifier, to handle big datasets. This work has gathered significant attention from the community because of its simplicity. It is very common in many other machine-learning strategies to find nearest neighbours and this normally time-consuming process can be sped up significantly using the proposed distributed solution.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
