Implementation in Apache Spark of the EM algorithm to estimate parameters of Fellegi-Sunter's canonical model of record linkage.
splink: Probabilistic record linkage and deduplication at scale
splink implements Fellegi-Sunter's canonical model of record linkage in Apache Spark, including EM algorithm to estimate parameters of the model.
The aims of
splink are to:
Work at much greater scale than current open source implementations (100 million records +).
Get results faster than current open source implementations - with runtimes of less than an hour.
Have a highly transparent methodology, so the match scores can be easily explained both graphically and in words
Have accuracy similar to some of the best alternatives
splink is a Python package. It uses the Spark Python API to execute data linking jobs in a Spark cluster. It has been tested in Apache Spark 2.3 and 2.4.
Install splink using
pip install splink
You can run demos of
splink in an interactive Jupyter notebook by clicking the button below:
The best documentation is currently a series of demonstrations notebooks in the splink_demos repo.
The statistical model behind
splink is the same as that used in the R fastLink package. Accompanying the fastLink package is an academic paper that describes this model. This is the best place to start for users wanting to understand the theory about how
You can read a short blog post about
You can find a short video introducing
splink and running though an introductory demo here.
A 'best practices and performance tuning' tutorial can be found here.
We are also very grateful to colleagues at the UK's Office for National Statistics for their expert advice and peer review of this work.
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