The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Making large-scale support vector machine learning practical
Advances in kernel methods
Data integration using similarity joins and a word-based information representation language
ACM Transactions on Information Systems (TOIS)
Record linkage: making maximum use of the discriminating power of identifying information
Communications of the ACM
Declarative Data Cleaning: Language, Model, and Algorithms
Proceedings of the 27th International Conference on Very Large Data Bases
Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning domain-independent string transformation weights for high accuracy object identification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to match and cluster large high-dimensional data sets for data integration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian decision model for cost optimal record matching
The VLDB Journal — The International Journal on Very Large Data Bases
Robust and efficient fuzzy match for online data cleaning
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A generalized cost optimal decision model for record matching
Proceedings of the 2004 international workshop on Information quality in information systems
A hierarchical graphical model for record linkage
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Adaptive Name Matching in Information Integration
IEEE Intelligent Systems
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Eliminating fuzzy duplicates in data warehouses
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Merging the results of approximate match operations
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Record-linkage is the process of identifying whether two separate records refer to the same real-world entity when some elements of the record’s identifying information (attributes) agree and others disagree. Existing record-linkage decision methodologies use the outcomes from the comparisons of the whole set of attributes. Here, we propose an alternative scheme that assesses the attributes sequentially, allowing for a decision to made at any attribute’s comparison stage, and thus before exhausting all available attributes. The scheme we develop is optimum in that it minimizes a well-defined average cost criterion while the corresponding optimum solution can be easily mapped into a decision tree to facilitate the record-linkage decision process. Experimental results performed in real datasets indicate the superiority of our methodology compared to existing approaches.