• DocumentCode
    2256169
  • Title

    An algorithm of locating order mining based on sequence number

  • Author

    Fang, Gang ; Ying, Hong ; Xiong, Jiang ; Zhao, Yong-jian

  • Author_Institution
    Chongqing Three Gorges Univ., Chongqing, China
  • Volume
    1
  • fYear
    2010
  • fDate
    11-14 July 2010
  • Firstpage
    403
  • Lastpage
    407
  • Abstract
    At present, existing association rules mining algorithms have redundant candidate frequent itemsets and repeated computing. This paper proposes an algorithm of locating order mining based on sequence number, which is suitable for mining long frequent itemsets. In order to fast search long frequent itemsets, the algorithm adopts not only traditional down search, but also the method of locating order of subset to generate candidate frequent itemsets. It has two aspects, which are different from traditional down search mining algorithm. One is that the algorithm need locate order of subsets of non frequent itemsets via down search. The other is that the algorithm uses character of attribute sequence number to compute support for only scanning database once. The algorithm may efficiently delete repeated L-candidate frequent itemsets generated by (L+1)-non frequent itemsets via locating subsets´ order, whose efficiency is improved. The result of experiment indicates that the algorithm is suitable for mining long frequent itemsets, and it is faster and more efficient than present algorithms of mining long frequent itemsets.
  • Keywords
    data mining; association rules mining; attribute sequence number; database scanning; down search mining; long frequent itemset mining; order mining; repeated computing; Algorithm design and analysis; Cybernetics; Itemsets; Machine learning; Machine learning algorithms; Tin; Association rules; Down search; Locating order; Long frequent itemsets; Sequence number;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-1-4244-6526-2
  • Type

    conf

  • DOI
    10.1109/ICMLC.2010.5581028
  • Filename
    5581028