The field of Record Linkage is concerned with identifying records from one or more datasets which refer to the same underlying entities. Where entity-unique identifiers are not available and errors occur, the process is non-trivial. Many techniques developed in this field require human intervention to set parameters, manually classify possibly matched records, or provide examples of matched and non-matched records. Whilst of great use and providing high quality results, the requirement of human input, besides being costly, means that if the parameters or examples are not produced or maintained properly, linkage quality will be compromised. The contributions of this paper are a critical discussion on the record linkage process, arguing for a more restrictive use of blocking in research, and evaluating and modifying the farthest first clustering technique to produce results close to a supervised technique.
|Cite as: Goiser, K. and Christen, P. (2006). Towards Automated Record Linkage. In Proc. Fifth Australasian Data Mining Conference (AusDM2006), Sydney, Australia. CRPIT, 61. Peter, C., Kennedy, P. J., Li, J., Simoff, S. J. and Williams, G. J., Eds. ACS. 23-31. |
(local if available)