  Accurate and efficient record linkage is an open challenge of particular
relevance to Australian Government Agencies, who recognise that so-called
wicked social problems are best tackled by forming partnerships founded on
large-scale data fusion. Names and addresses are the most common attributes on
which data from different government agencies can be linked. In this paper, we
focus on the problem of address linking. Linkage is particularly problematic
when the data has significant quality issues. The most common approach for
dealing with quality issues is to standardise raw data prior to linking. If a
mistake is made in standardisation, however, it is usually impossible to
recover from it to perform linkage correctly. This paper proposes a novel
algorithm for address linking that is particularly practical for linking large
disparate sets of addresses, being highly scalable, robust to data quality
issues and simple to implement. It obviates the need for labour intensive and
problematic address standardisation. We demonstrate the efficacy of the
algorithm by matching two large address datasets from two government agencies
with good accuracy and computational efficiency.
