A hierarchical model-based approach to co-clustering high-dimensional data (Contributo in atti di convegno)

Type
Label
  • A hierarchical model-based approach to co-clustering high-dimensional data (Contributo in atti di convegno) (literal)
Anno
  • 2008-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/1363686.1363891 (literal)
Alternative label
  • Costa Gianni; Giuseppe Manco; Riccardo Ortale (2008)
    A hierarchical model-based approach to co-clustering high-dimensional data
    in ACM SAC 2008, Fortaleza
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Costa Gianni; Giuseppe Manco; Riccardo Ortale (literal)
Pagina inizio
  • 886 (literal)
Pagina fine
  • 890 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?doid=1363686.1363891 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
  • Google Scholar (literal)
  • DBLP (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ICAR-CNR; ICAR-CNR; ICAR-CNR (literal)
Titolo
  • A hierarchical model-based approach to co-clustering high-dimensional data (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-59593-753-7 (literal)
Abstract
  • We propose a hierarchical, model-based co-clustering framework for handling high-dimensional datasets. The technique views the dataset as a joint probability distribution over row and column variables. Our approach starts by clustering tuples in a dataset, where each cluster is characterized by a different probability distribution. Subsequently, the conditional distribution of attributes over tuples is exploited to discover natural co-clusters in the data. An intensive empirical evaluation highlights the effectiveness of our approach. (literal)
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