Using metric space indexing for complete and efficient record linkage
- Submitting institution
-
University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 255998823
- Type
- E - Conference contribution
- DOI
-
10.1007/978-3-319-93040-4_8
- Title of conference / published proceedings
- Advances in Knowledge Discovery and Data Mining : 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III
- First page
- 89
- Volume
- 10939 LNCS
- Issue
- -
- ISSN
- 0302-9743
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- 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
-
3
- Research group(s)
-
B - Systems
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper demonstrates the efficacy of similarity search techniques, in particular metric space indexing, on the problem of record linkage. It is significant because it presents a way of improving computational efficiency without sacrificing completeness (a common trade-off in existing approaches). In addition, the approach significantly reduces the amount of manual tuning and configuration required, while yielding better linkage quality than incomplete approaches.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
