• DocumentCode
    2480418
  • Title

    Efficiently Mining the Recent Frequent Patterns over Online Data Streams

  • Author

    Chen, Hui

  • Author_Institution
    Sch. of Software & Commun. Eng., Jiangxi Univ. of Finance & Econ., Nanchang, China
  • fYear
    2010
  • fDate
    22-23 May 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    In order to mine the frequent patterns in the recent time windows of the data stream than the historic, a new method was proposed to mine the recent frequent patterns in the sliding window of an online data stream. It calculated the approximate frequencies of patterns in the sliding window with a conservative strategy. Also, it built a Recent Frequent Pattern tree(RFP-tree for short) to incrementally capture the patterns in the sliding window and mine them by scanning the stream only once. Extensive experiments show that the proposed method is more efficient and scalable than other analogous algorithms.
  • Keywords
    data mining; pattern classification; frequent patterns mining; online data streams sliding window; recent frequent pattern tree; Data engineering; Data mining; Data structures; Design methodology; Finance; Frequency; History; Itemsets; Monitoring; Tree data structures;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-5872-1
  • Electronic_ISBN
    978-1-4244-5874-5
  • Type

    conf

  • DOI
    10.1109/IWISA.2010.5473377
  • Filename
    5473377