• DocumentCode
    3222455
  • Title

    Analysis and Research for Network Management Alarms Correlation Based on Sequence Clustering Algorithm

  • Author

    Hou Sizu ; Zhang Xianfei

  • Author_Institution
    Dept. of Electron. & Commun. Eng., North China Electr. Power Univ., Baoding
  • Volume
    1
  • fYear
    2008
  • fDate
    20-22 Oct. 2008
  • Firstpage
    982
  • Lastpage
    986
  • Abstract
    The alarms correlation rules obtained on the bases of network management alarms play an important role on network management and network maintenance. Alarms correlation is a difficult problem in network fault management; sequential pattern mining can be utilized to extract episode rules from network system alarms. This paper introduces the related studies of alarms association and sequential pattern mining; describes the features of network management alarms; presents a method of mining correlation rules of network management alarms by sequence clustering algorithm. The experiments show that many interesting correlation rules could be acquired efficiently. Furthermore, these rules could be used to guide the intelligent network alarms filtering and fault location.
  • Keywords
    data mining; telecommunication computing; telecommunication network management; alarms correlation rules; fault location; intelligent network alarms filtering; network fault management; network maintenance; sequence clustering algorithm; sequential pattern mining; Algorithm design and analysis; Clustering algorithms; Conference management; Data mining; Energy management; Engineering management; Intelligent networks; Itemsets; Power system management; Technology management; Sequence Clustering; alarms correlation; correlation rules; data mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on
  • Conference_Location
    Hunan
  • Print_ISBN
    978-0-7695-3357-5
  • Type

    conf

  • DOI
    10.1109/ICICTA.2008.263
  • Filename
    4659635