Stochastic record linkage
Given the features of two records/entities, the job of stochastic record linkage is to give a measure of the closeness of the two entities. The final job is to find if the two records refer to the same entity. This can be accomplished by building a threshold-based classifier based on the weights.
We will show how to leverage two methods, emWeights and epiWeights, implemented in the RecordLinkage package.
Expectation maximization method
The method, emWeights, is based on the expectation maximization algorithm to derive from the weights, a measure of the closeness of two entities. According to this method, two conditional probabilities, one for match and an other for no match, has to be derived.
P (features | match = 0) and P (features | match = 1) are estimated using the expectation maximization algorithm. The weights are calculated as the ratio of these two probabilities. This approach is called the Fellegi-Sunter model.
> library(RecordLinkage) > data("RLdata500...
