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doi: 10.1007/bf03040962
handle: 10261/162481
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.
Josep Domingo-Ferrer and Vicenç Torra are partially supported by the EU project CASC: Contract: IST-2000-25069 and CICYT project STREAMOBILE
Peer Reviewed
Database theory, Knowledge discovery in databases, Record selection, Multi-database Mining, Re-identification algorithms, Record linkage, Chance discovery, Knowledge Discovery in Databases, Multi-database mining, Data Mining, Record Selection, Record Linkage, Chance Discovery, Data mining, Re-identification Algorithms
Database theory, Knowledge discovery in databases, Record selection, Multi-database Mining, Re-identification algorithms, Record linkage, Chance discovery, Knowledge Discovery in Databases, Multi-database mining, Data Mining, Record Selection, Record Linkage, Chance Discovery, Data mining, Re-identification Algorithms
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