Record linkage: making maximum use of the discriminating power of identifying information
Communications of the ACM
Introduction to Algorithms: A Creative Approach
Introduction to Algorithms: A Creative Approach
A Bayesian decision model for cost optimal record matching
The VLDB Journal — The International Journal on Very Large Data Bases
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Optimal Stopping: A Record-Linkage Approach
Journal of Data and Information Quality (JDIQ)
| Hi-index | 0.00 |
Record (or entity) matching or linkage is the process of identifying records in one or more data sources, that refer to the same real world entity or object. In record linkage, the ultimate goal of a decision model is to provide the decision maker with a tool for making decisions upon the actual matching status of a pair of records (i.e., documents, events, persons, cases, etc.). Existing models of record linkage rely on decision rules that minimize the probability of subjecting a case to clerical review, conditional on the probabilities of erroneous matches and erroneous non-matches. In practice though, (a) the value of an erroneous match is, in many applications, quite different from the value of an erroneous non-match, and (b) the cost and the probability of a misclassification, which is associated with the clerical review, is ignored in this way. In this paper, we present a decision model which is optimal, based on the cost of the record linkage operation, and general enough to accommodate multi-class or multi-decision case studies. We also present an example along with the results from applying the proposed model to large comparison spaces.