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IDEAL
2005
Springer

Probabilistic Data Generation for Deduplication and Data Linkage

10 years 3 months ago
Probabilistic Data Generation for Deduplication and Data Linkage
Abstract. In many data mining projects the data to be analysed contains personal information, like names and addresses. Cleaning and preprocessing of such data likely involves deduplication or linkage with other data, which is often challenged by a lack of unique entity identifiers. In recent years there has been an increased research effort in data linkage and deduplication, mainly in the machine learning and database communities. Publicly available test data with known deduplication or linkage status is needed so that new linkage algorithms and techniques can be tested, evaluated and compared. However, publication of data containing personal information is normally impossible due to privacy and confidentiality issues. An alternative is to use artificially created data, which has the advantages that content and error rates can be controlled, and the deduplication or linkage status is known. Controlled experiments can be performed and replicated easily. In this paper we present a f...
Peter Christen
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where IDEAL
Authors Peter Christen
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