The Optimization and Improvement of the Apriori Algorithm

Through the study of Apriori algorithm we discover two aspects that affect the efficiency of the algorithm. One is the frequent scanning database, the other is large scale of the candidate itemsets. Therefore, IApriori algorithm is proposed that can reduce the times of scanning database, optimize the join procedure of frequent itemsets generated in order to reduce the size of the candidate itemsets. The results show that the algorithm is better than Apriori algorithm.

[1]  Peng Gong,et al.  The Application of Improved Association Rules Data Mining Algorithm Apriori in CRM , 2007, 2007 2nd International Conference on Pervasive Computing and Applications.

[2]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[3]  Yang Xiao-ping.  Improvement of Apriori Algorithm for Association Rules , 2006 .

[4]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

[5]  S. da Silva Camargo,et al.  MiRABIT: A new algorithm for mining association rules , 2002, 12th International Conference of the Chilean Computer Science Society, 2002. Proceedings..

[6]  Yang Cheng,et al.  The Research of Improved Apriori Algorithm for Mining Association Rules , 2007, 2007 International Conference on Service Systems and Service Management.

[7]  Feng Qian,et al.  A Method of Improvement and Optimization on Association Rules Apriori Algorithm , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[8]  Zhen Zhu,et al.  Book Recommendation Service by Improved Association Rule Mining Algorithm , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[9]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Yuan Jim.  A Survey of Association Rules of Apriori Algorithms , 2004 .

[11]  Shamkant B. Navathe,et al.  Mining for strong negative associations in a large database of customer transactions , 1998, Proceedings 14th International Conference on Data Engineering.

[12]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.