The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Robust and efficient fuzzy match for online data cleaning
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Cleaning the Spurious Links in Data
IEEE Intelligent Systems
Iterative record linkage for cleaning and integration
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Reference reconciliation in complex information spaces
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Exploiting relationships for object consolidation
Proceedings of the 2nd international workshop on Information quality in information systems
Relational clustering for multi-type entity resolution
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Eliminating fuzzy duplicates in data warehouses
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Object Distinction Based on Decision Tree
ITCS '09 Proceedings of the 2009 International Conference on Information Technology and Computer Science - Volume 01
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In many applications several references may refer to one real entity, the task of reference reconciliation is to group those references into several clusters so that each cluster associates with only one real entity. In this paper we propose a new method for reference reconciliation, that is, in addition to the traditional attribute values similarity, we employ the record-level relationships to compute the association similarity values of references in graphs, then we combine this kind of similarity with the traditional attribute values similarity and use the clustering algorithm to group the closest references.