Effective Incremental Clustering for Duplicate Detection in Large Databases (Contributo in atti di convegno)

Type
Label
  • Effective Incremental Clustering for Duplicate Detection in Large Databases (Contributo in atti di convegno) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/IDEAS.2006.18 (literal)
Alternative label
  • Francesco Folino; Giuseppe Manco; Luigi Pontieri (2006)
    Effective Incremental Clustering for Duplicate Detection in Large Databases
    in 10th International Database Engineering and Applications Symposium (IDEAS 2006), Delhi
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Francesco Folino; Giuseppe Manco; Luigi Pontieri (literal)
Pagina inizio
  • 45 (literal)
Pagina fine
  • 52 (literal)
Note
  • IEEE Xplore digital library (literal)
  • DBLP (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di calcolo e reti ad alte prestazioni; Istituto di calcolo e reti ad alte prestazioni; Istituto di calcolo e reti ad alte prestazioni (literal)
Titolo
  • Effective Incremental Clustering for Duplicate Detection in Large Databases (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 0-7695-2577-6 (literal)
Abstract
  • We propose an incremental algorithm for discovering clusters of duplicate tuples in large databases. The core of the approach is the usage of an indexing technique which, for any newly arrived tuple mu, allows to efficiently retrieve a set of tuples in the database which are mostly similar to P, and which are likely to refer to the same real-world entity which is associated with mu. The proposed index is based on a hashing approach which tends to assign similar objects to the same buckets. Empirical and analytical evaluation demonstrates that the proposed approach achieves satisfactory efficiency results, at the cost of low accuracy loss. (literal)
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