Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
A vector space model for automatic indexing
Communications of the ACM
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Efficient Record Linkage in Large Data Sets
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
TAILOR: A Record Linkage Tool Box
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth 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
DogmatiX tracks down duplicates in XML
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
A Fast Linkage Detection Scheme for Multi-Source Information Integration
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Adaptive Blocking: Learning to Scale Up Record Linkage
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data integration with uncertainty
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Introduction to Information Retrieval
Introduction to Information Retrieval
Accurate Synthetic Generation of Realistic Personal Information
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Learning blocking schemes for record linkage
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Frequent Itemset Mining for Clustering Near Duplicate Web Documents
ICCS '09 Proceedings of the 17th International Conference on Conceptual Structures: Conceptual Structures: Leveraging Semantic Technologies
An Introduction to Duplicate Detection
An Introduction to Duplicate Detection
Evaluating entity resolution results
Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment
Large-scale collective entity matching
Proceedings of the VLDB Endowment
Efficient SPectrAl Neighborhood blocking for entity resolution
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Learning-based entity resolution with MapReduce
Proceedings of the third international workshop on Cloud data management
A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication
IEEE Transactions on Knowledge and Data Engineering
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Entity resolution is the process of discovering groups of tuples that correspond to the same real-world entity. Blocking algorithms separate tuples into blocks that are likely to contain matching pairs. Tuning is a major challenge in the blocking process and in particular, high expertise is needed in contemporary blocking algorithms to construct a blocking key, based on which tuples are assigned to blocks. In this work, we introduce a blocking approach that avoids selecting a blocking key altogether, relieving the user from this difficult task. The approach is based on maximal frequent itemsets selection, allowing early evaluation of block quality based on the overall commonality of its members. A unique feature of the proposed algorithm is the use of prior knowledge of the estimated size of duplicate sets in enhancing the blocking accuracy. We report on a thorough empirical analysis, using common benchmarks of both real-world and synthetic datasets to exhibit the effectiveness and efficiency of our approach.