• DocumentCode
    2921369
  • Title

    Finding Frequent Item Sets from Sparse Matrix

  • Author

    Zheng Xiao-yan ; Sun Ji-Zhou

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Tianjin Univ., Tianjin
  • fYear
    2009
  • fDate
    20-22 Feb. 2009
  • Firstpage
    615
  • Lastpage
    619
  • Abstract
    According to the features of sparse data source while mining association rules, the paper designs a special linked-list unit and two strategies to store data in matrix. A novel algorithm, called SMM (Sparse-Matrix Mining), is proposed to find large item sets from sparse matrix. SMM maps database into a binary sparse matrix and stores compressed data into a linked-list, from which to find large item sets. It uses less I/O and computational time in mining. Experiments show that SMM finds large item sets efficiently and is well scalable.
  • Keywords
    data compression; data mining; set theory; sparse matrices; binary sparse matrix; computational time; frequent item sets; sparse data source; sparse-matrix mining; Association rules; Computer science; Data mining; Educational technology; Electronic mail; Itemsets; Paper technology; Sparse matrices; Sun; Transaction databases; Compress by Column; frequent item sets; linked-list; sparse matrix;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronic Computer Technology, 2009 International Conference on
  • Conference_Location
    Macau
  • Print_ISBN
    978-0-7695-3559-3
  • Type

    conf

  • DOI
    10.1109/ICECT.2009.69
  • Filename
    4796037