An incremental clustering scheme for duplicate detection in large databases

Proceedings of the International Database Engineering and Applications Symposium, IDEAS

Volumn 2005-January, Issue January, 2005, Pages 89-95

  Cesario, Eugenio ^a   Folino, Francesco ^a   Manco, Giuseppe ^a   Pontieri, Luigi ^a  

^a ICAR CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLUSTERING ALGORITHMS;

DUPLICATE DETECTION; EFFICIENCY IMPROVEMENT; EMPIRICAL EVALUATIONS; INCREMENTAL ALGORITHM; INCREMENTAL CLUSTERING; INDEX STRUCTURE; INDEXING TECHNIQUES; REAL-WORLD ENTITIES;

DATABASE SYSTEMS;

EID: 48349111306     PISSN: 10988068     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IDEAS.2005.10     Document Type: Conference Paper

References (13)

1. M. Bilenko et al. Adaptive name matching in information integration. IEEE Intelligent Systems, 18(5):16-23, 2003.
2. M. Bilenko and R. J. Mooney. Adaptive duplicate detection using learnable string similarity measures. In Proc. 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 39-48, 2003.
3. A. Broder, S. Glassman, M. Manasse, and G. Zweig. Syntactic clustering on the web. In Proc. 6th Int.WWW Conf., pages 1157-1166, 1997.
4. P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. In Proc. of the 23rd Int. Conf. on Very Large Data Bases, pages 426-435, 1997.
5. W. Cohen and J. Richman. Learning to match and cluster entity names. In Proc. ACM SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval, 2001.
6. W. W. Cohen, P. Ravikumar, and S. E. Fienberg. A comparison of string distance metrics for name-matching tasks. In Proc. IJCAI Workshop on Information Integration on the Web, pages 73-78, 2003.
7. W. W. Cohen and J. Richman. Learning to match and cluster large high-dimensional data sets for data integration. In Proc. 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 475-480, 2002.
8. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In Proc. 25th Conf. on Very Large Data Bases, pages 518-529, 1999.
9. P. Indyk and R. Motwani. Approximate nearest neighbor-towards removing the curse of dimensionality. In Proc. 30th Symposium on Theory of Computing, pages 604-613, 1998.
10. A. K. McCallum, K. Nigam, and L. Ungar. Efficient clustering of high-dimensional data sets with application to reference matching. In Proc. 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 169-178, 2000.
11. A. E. Monge and C. P. Elkan. The field matching problem: Algorithms and applications. In Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, pages 267-270, 1996.
12. A. E. Monge and C. P. Elkan. An efficient domain-independent algorithm for detecting approximately duplicate database records. In Proc. SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pages 23-29, 1997.
13. S. Sarawagi and A. Bhamidipaty. Interactive deduplication using active learning. In Proc. 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 269-278, 2002.