Selecting potentially relevant records using re-identification methods
This work proposes re-identification algorithms to select records that are interesting from the point of view of giving new information. Instead of focusing on re-identified elements, we focus on non re-identified records (non linked records) as they are the ones that potentially supply new and relevant information. Moreover, these relevant characteristics can correspond to chances for improving the knowledge of a system. To evaluate our approach, we have applied it to a example using publicly available data from the UCI repository. We have used the data of the ionosphere data, base to build a re-identification problem for 35 non-common variables. We show that the use of a simple heuristic rule base can effectively select potentially interesting records.
re-identification algorithms chance discovery knowledge discovery in databases data mining multi-database mining record selection record linkage