Our great sponsors
-
splink
Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
Record linkage has been a big part of a project I've been working on for 6 months now. I personally think a great and free solution be using the splink package in Python which can handle 10+m rows which implements the Fellegi-Sunter model (equivalent to a naive-Bayes model) is the classical model in record linkage. It can be trained in an unsupervised manner using some initial parameter estimation (these are quite intuitive) and then expectation maximisation. The features in the model will be different pairwise string comparisons on your field of interest. These can include exact equality; edit distance comparisons like Levensthein distance and Jaro-Winkler; and phonetic comparisons like soundex and double metaphone. The splink pacakge will handle training the model and then all the graph theory at the end to connect all your links into clusters. All the details you'll need are in the links. https://www.robinlinacre.com/probabilistic\_linkage/ https://moj-analytical-services.github.io/splink/
Related posts
- Splink: Fast, accurate, scalable probabilistic data linkage
- Ask HN: What projects are you working on?
- Conformed Dimensions problem that keeps recurring on every project
- How do you join two sources with attributes that aren't identical?
- Splink 3: Fast, accurate and scalable record linkage (entity resolution) in Python