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A scaling approach to record linkage
Goldstein, Harvey Harron, Katie Cortina-Borja, Mario
Goldstein, Harvey
Harron, Katie
Cortina-Borja, Mario
Abstract
With increasing availability of large datasets derived from administrative and other sources, there is an increasing demand for the successful linking of these to provide rich sources of data for further analysis. Variation in the quality of identifiers used to carry out linkage means that existing approaches are often based upon ‘probabilistic’ models, which are based on a number of assumptions, and can make heavy computational demands. In this paper, we suggest a new approach to classifying record pairs in linkage, based upon weights (scores) derived using a scaling algorithm. The proposed method does not rely on training data, is computationally fast, requires only moderate amounts of storage and has intuitive appeal.
Keywords
Date
2017
Type
Journal article
Journal
Statistics in Medicine
Book
Volume
36
Issue
16
Page Range
2514-2521
Article Number
ACU Department
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DOI
Source URL
Event URL
Open Access Status
License
File Access
Controlled