Volume 34 Issue 8
Aug.  2008
Turn off MathJax
Article Contents
Liu Chun, Zheng Zheng, Cai Kaiyuan, et al. Online mining frequent closed itemsets over data stream[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(8): 969-972. (in Chinese)
Citation: Liu Chun, Zheng Zheng, Cai Kaiyuan, et al. Online mining frequent closed itemsets over data stream[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(8): 969-972. (in Chinese)

Online mining frequent closed itemsets over data stream

  • Received Date: 20 Jul 2007
  • Publish Date: 31 Aug 2008
  • Based on the algorithm LossCounting, a novel approach called LossyCounting_Closed(LC_Closed ) for mining closed frequent itemsets over data stream was proposed. A new summary data structure called Closed-Itemsets-forest (CI-forest) was developed for maintaining only closed frequent itemsets.The insertion and query of closed itemsets can be rapidly made based on the data structure CI-forest, and the location of the associated historical closed itemsets in the stage of dealing with the new transaction is also facilitated by CI-forest. Since the algorithm maintains closed itemsets online, the current closed frequent itemsets can be output in real time based on user-s specified thresholds. The effectiveness of the proposed method is shown in the experimental results.

     

  • loading
  • [1] Wang J, Han J, Pei J. CLOSET+: searching for the best strategies for mining frequent closed itemsets SIGKDD-03.WDC,USA:ACM,2003: 236-245 [2] Babcock B, Babu S, Datar M, et al. Models and issues in data stream systems ACM PODS-02. Madison, USA: ACM, 2002:1-16 [3] Manku G,Motw R.Approximate frequency counts over data streams Proc 28th Int Conf of VLDB. Hongkong, China: Morgan Kanfmann, 2002: 346-357 [4] Gosta G, Zhu J. Efficiently using prefix-trees in mining frequent itemsets Proc of IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI-03). 2003
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views(3374) PDF downloads(1001) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return