Approximate mining of frequent patterns on streams (Articolo in rivista)

Type
Label
  • Approximate mining of frequent patterns on streams (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Alternative label
  • Silvestri C.; Orlando S. (2007)
    Approximate mining of frequent patterns on streams
    in Intelligent data analysis; IOS Press, Amsterdam (Paesi Bassi)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Silvestri C.; Orlando S. (literal)
Pagina inizio
  • 49 (literal)
Pagina fine
  • 73 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?id=1367489.1367493 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 11 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: Intelligent Data Analysis, vol. 11 (1) pp. 49 - 73. IOS Press, 2007. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Dipartimento di Informatica, Università Ca' Foscari di Venezia CNR-ISTI, Pisa (literal)
Titolo
  • Approximate mining of frequent patterns on streams (literal)
Abstract
  • Many critical applications, like intrusion detection or stock market analysis, require a nearly immediate result based on a continuous and infinite stream of data. In most cases finding an exact solution is not compatible with limited availability of resources and real time constraints, but an approximation of the exact result is enough for most purposes. This paper introduces a new algorithm for approximate mining of frequent itemsets from streams of transactions using a limited amount of memory. The proposed algorithm is based on the computation of frequent itemsets in recent data and an effective method for inferring the global support of previously infrequent itemsets. Both upper and lower bounds on the support of each pattern found are returned along with the interpolated support. An extensive experimental evaluation shows that AP_Stream, the proposed algorithm, yields a good approximation of the exact global result considering both the set of patterns found and their supports. (literal)
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