• DocumentCode
    3182103
  • Title

    Mining Association Rules Based on Apriori Algorithm and Application

  • Author

    Pei-ji Wang ; Lin Shi ; Jin-niu Bai ; Yu-lin Zhao

  • Author_Institution
    Sch. of Math., Phys. & Biol. Eng., Inner Mongolia Univ. of Sci. & Technol., Baotou, China
  • Volume
    1
  • fYear
    2009
  • fDate
    25-27 Dec. 2009
  • Firstpage
    141
  • Lastpage
    143
  • Abstract
    In the data mining research, mining association rules is an important topic. Apriori algorithm submitted by Agrawal and R. Srikant in 1994 is the most effective algorithm. Aimed at two problems of discovering frequent itemsets in a large database and mining association rules from frequent itemsets, the author makes some research on mining frequent itemsets algorithm based on apriori algorithm and mining association rules algorithm based on improved measure system. Mining association rules algorithm based on support, confidence and interestingness is improved, aiming at creating interestingness useless rules and losing useful rules. Useless rules are cancelled, creating more reasonable association rules including negative items. The above method is used to mine association rules to the 2002 student score list of computer specialized field in Inner Mongolia university of science and technology.
  • Keywords
    data mining; very large databases; apriori algorithm; association rule mining; data mining; frequent itemset discovery; interestingness useless rule; large database; losing useful rule; measure system; Application software; Association rules; Biology computing; Computer applications; Data mining; Databases; Information systems; Itemsets; Mathematics; Physics computing; application; apriori algorithm; association rules mining; recognizable matrix;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-0-7695-3930-0
  • Electronic_ISBN
    978-1-4244-5423-5
  • Type

    conf

  • DOI
    10.1109/IFCSTA.2009.41
  • Filename
    5385112