• DocumentCode
    2710495
  • Title

    Discovering Significant Patterns in Multi-stream Sequences

  • Author

    Gwadera, Robert ; Crestani, Fabio

  • Author_Institution
    Fac. of Inf., Univ. of Lugano, Lugano
  • fYear
    2008
  • fDate
    15-19 Dec. 2008
  • Firstpage
    827
  • Lastpage
    832
  • Abstract
    Discovering significant patterns in synchronized multi-stream sequences also known as multi-attribute event sequences (multi-sequences), is an important problem in many domains, including monitoring systems and information retrieval. In this paper we propose a new approach for assessing significance of multi-stream patterns in multi-attribute event sequences. In experiments on physiological multi-stream data we show applicability of our method.
  • Keywords
    information retrieval; information retrieval; monitoring systems; multi-attribute event sequences; multi-stream sequences; Biomedical monitoring; Data mining; Informatics; Information retrieval; Joining processes; Patient monitoring; Sensor phenomena and characterization; Sensor systems and applications; Telecommunication traffic; Time factors; multi-stream data mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on
  • Conference_Location
    Pisa
  • ISSN
    1550-4786
  • Print_ISBN
    978-0-7695-3502-9
  • Type

    conf

  • DOI
    10.1109/ICDM.2008.146
  • Filename
    4781186