• DocumentCode
    2334418
  • Title

    Efficiently mining maximal frequent itemsets

  • Author

    Gouda, Karam ; Zaki, Mohammed

  • Author_Institution
    Comput. Sci. & Commun. Eng. Dept, Kyushu Univ., Japan
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    163
  • Lastpage
    170
  • Abstract
    We present GenMax, a backtracking search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation. Systematic experimental comparison with previous work indicates that different methods have varying strengths and weaknesses based on dataset characteristics. We found GenMax to be a highly efficient method to mine the exact set of maximal patterns
  • Keywords
    backtracking; data mining; optimisation; GenMax; backtrack search based algorithm; dataset; diffset propagation; efficient maximal frequent itemset mining; maximality checking; optimizations; progressive focusing; search space pruning; Association rules; Computer science; Data mining; Frequency; Itemsets; Transaction databases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
  • Conference_Location
    San Jose, CA
  • Print_ISBN
    0-7695-1119-8
  • Type

    conf

  • DOI
    10.1109/ICDM.2001.989514
  • Filename
    989514
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2334418