Algorithms for clustering data
Algorithms for clustering data
The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
The Inter-Database Instance Identification Problem in Integrating Autonomous Systems
Proceedings of the Fifth International Conference on Data Engineering
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
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Identification of Fuzzy Duplicates
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Leveraging aggregate constraints for deduplication
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Swoosh: a generic approach to entity resolution
The VLDB Journal — The International Journal on Very Large Data Bases
Data centric research at the University of Queensland
ACM SIGMOD Record
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The aim of duplicate detection is to group records in a relation which refer to the same entity in the real world such as a person or business. Most existing works require user specified parameters such as similarity threshold in order to conduct duplicate detection. These methods are called user-first in this paper. However, in many scenarios, pre-specification from the user is very hard and often unreliable, thus limiting applicability of user-first methods. In this paper, we propose a user-last method, called Active Duplicate Detection (ADD), where an initial solution is returned without forcing user to specify such parameters and then user is involved to refine the initial solution. Different from user-first methods where user makes decision before any processing, ADD allows user to make decision based on an initial solution. The identified initial solution in ADD enjoys comparatively high quality and is easy to be refined in a systematic way (at almost zero cost).