Efficient mining of temporally annotated sequences (Contributo in atti di convegno)

Type
Label
  • Efficient mining of temporally annotated sequences (Contributo in atti di convegno) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Alternative label
  • Giannotti F.; Nanni M.; Pedreschi D. (2006)
    Efficient mining of temporally annotated sequences
    in 2006 SIAM Conference on Data Mining, Bethesda, Washington D.C., USA
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Giannotti F.; Nanni M.; Pedreschi D. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: 2006 SIAM Conference on Data Mining (Bethesda, Washington D.C., USA, 20-22 April 2006). Proceedings, pp. 348-359. Joydeep Ghosh, Diane Lambert, David Skillicorn and Jaideep Srivastava (eds.). SIAM, 2006. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • ABSTRACT: Sequential patterns mining received much attention in recent years, thanks to its various potential application domains. A large part of them represent data as collections of time-stamped itemsets, e.g., customers' purchases, logged web accesses, etc. Most approaches to sequence mining focus on sequentiality of data, using time-stamps only to order items and, in some cases, to constrain the temporal gap between items. In this paper, we propose an e±cient algorithm for computing (temporally-)annotated sequential patterns, i.e., sequential patterns where each transition is annotated with a typical transition time derived from the source data. The algorithm adopts a prefix-projection approach to mine candidate sequences, and it is tightly integrated with a annotation mining process that associates sequences with temporal annotations. The pruning capabilities of the two steps sum together, yielding significant improvements in performances, as demonstrated by a set of experiments performed on synthetic datasets. (literal)
Titolo
  • Efficient mining of temporally annotated sequences (literal)
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