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A scaling approach to record linkage
Author(s) -
Goldstein Harvey,
Harron Katie,
CortinaBorja Mario
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7287
Subject(s) - computer science , linkage (software) , probabilistic logic , identifier , record linkage , variation (astronomy) , data mining , scaling , quality (philosophy) , artificial intelligence , mathematics , population , physics , demography , geometry , philosophy , epistemology , sociology , astrophysics , gene , programming language , biochemistry , chemistry
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. Copyright © 2017 John Wiley & Sons, Ltd.