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A scaling approach to record linkage.

Harvey Goldstein1,2, Katie Harron3, Mario Cortina-Borja2

  • 1University of Bristol, Bristol, U.K.

Statistics in Medicine
|March 18, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel, computationally fast method for linking large datasets using a scaling algorithm. It offers an intuitive approach to classifying record pairs without needing training data, improving data analysis efficiency.

Keywords:
correspondence analysisdata linkagerecord linkagescaling

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Area of Science:

  • Data Science
  • Computer Science
  • Information Science

Background:

  • Growing availability of large administrative datasets necessitates effective data linkage.
  • Existing probabilistic record linkage methods rely on assumptions and are computationally intensive.

Purpose of the Study:

  • To propose a new, efficient method for classifying record pairs in data linkage.
  • To overcome limitations of traditional probabilistic linkage models.

Main Methods:

  • A novel approach using a scaling algorithm to derive weights (scores) for classifying record pairs.
  • Classification of record pairs based on calculated weights.

Main Results:

  • The proposed method is computationally fast and requires moderate storage.
  • It does not rely on training data and offers intuitive appeal.
  • Successfully classifies record pairs for enhanced data linkage.

Conclusions:

  • The new scaling algorithm-based method provides an efficient and accessible alternative for record linkage.
  • This approach facilitates the creation of rich data sources for further analysis.