  Record linkage involves merging records in large, noisy databases to remove
duplicate entities. It has become an important area because of its widespread
occurrence in bibliometrics, public health, official statistics production,
political science, and beyond. Traditional linkage methods directly linking
records to one another are computationally infeasible as the number of records
grows. As a result, it is increasingly common for researchers to treat record
linkage as a clustering task, in which each latent entity is associated with
one or more noisy database records. We critically assess performance bounds
using the Kullback-Leibler (KL) divergence under a Bayesian record linkage
framework, making connections to Kolchin partition models. We provide an upper
bound using the KL divergence and a lower bound on the minimum probability of
misclassifying a latent entity. We give insights for when our bounds hold using
simulated data and provide practical user guidance.
