dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data.
dedupe will help you:
dedupe takes in human training data and comes up with the best rules for your dataset to quickly and automatically find similar records, even with very large databases.
If you or your organization would like professional assistance in working with the dedupe library, Dedupe.io LLC offers consulting services. Read more about pricing and available services here.
A cloud service powered by the dedupe library for de-duplicating and finding matches in your data. It provides a step-by-step wizard for uploading your data, setting up a model, training, clustering and reviewing the results.
If you only want to use dedupe, install it this way:
pip install dedupe
Once you have virtualenvwrapper set up,
mkvirtualenv dedupe git clone git://github.com/dedupeio/dedupe.git cd dedupe pip install "numpy>=1.9" pip install -r requirements.txt cython src/*.pyx pip install -e .
If these tests pass, then everything should have been installed correctly!
Afterwards, whenever you want to work on dedupe,
Unit tests of core dedupe functions
Using Record Linkage
Dedupe is based on Mikhail Yuryevich Bilenko's Ph.D. dissertation: Learnable Similarity Functions and their Application to Record Linkage and Clustering.
If something is not behaving intuitively, it is a bug, and should be reported. Report it here
Copyright (c) 2019 Forest Gregg and Derek Eder. Released under the MIT License.
Third-party copyright in this distribution is noted where applicable.
If you use Dedupe in an academic work, please give this citation:
Forest Gregg and Derek Eder. 2019. Dedupe. https://github.com/dedupeio/dedupe.