• DocumentCode
    2427892
  • Title

    Mining Frequent Items Based on Bloom Filter

  • Author

    Wang, Shuyun ; Hao, Xiulan ; Xu, Hexiang ; Hu, Yunfa

  • Author_Institution
    Fudan Univ., Shanghai
  • Volume
    4
  • fYear
    2007
  • fDate
    24-27 Aug. 2007
  • Firstpage
    679
  • Lastpage
    683
  • Abstract
    This paper introduce the algorithm MIBFD (mining frequent items using bloom filter based on damped model) for mining recent frequent items in data streams. Based on an efficient data structure named extensible and scalable bloom filter(ESBF), MIBFD is able to adjust the size of memory used dynamically. Theoretical analysis and experiments show that MIBFD is efficient both in processing time and in memory usage.
  • Keywords
    data mining; bloom filter; damped model; data streams; data structure; Counting circuits; Data mining; Data structures; Fading; Filters; Frequency estimation; Frequency shift keying; Information filtering; Monitoring; Sampling methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on
  • Conference_Location
    Haikou
  • Print_ISBN
    978-0-7695-2874-8
  • Type

    conf

  • DOI
    10.1109/FSKD.2007.400
  • Filename
    4406473
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2427892