Abstract
Aiming at the weakness of traditional Apriori algorithm, this paper presents MFI algorithm for mining maximum frequent itemsets on association rules. MFI algorithm scans database only once, the algorithm need not produce candidate itemsets, MFI algorithm does not use the method of iteration for each layer, MFI algorithm adopts binary bit and logic operation.The efficiency is distinctly improved in mining maximum frequent itemset.
Publisher
Trans Tech Publications, Ltd.
Reference13 articles.
1. Agrawa lR, Imielinski T, Swami A. Mining association rules between sets of items in large databases (C). In: Buneman P, Jajodia S, eds. Proc. of the ACM SIGMOD Conf. on Management of Data (SIGMOD'93). New York: ACM Press, 1993. 207-216.
2. Agrawa lR, Srikant R. Fast algorithms for mining association rules in large databases. In: Bocca JB, Jarke M, Zaniolo C, eds. Proc. of the 20th Int'l Conf. on Very Large Data Bases. Santiago: Morgan Kaufmann, 1994. 478-499.
3. Aly HH, Taha Y, Amr AA. Fast mining of association rules in large-scale problems. In: Abdel-Wahab H, Jeffay K, eds. Proc. of the 6th IEEE Symp. on Computers and Communications (ISCC 2001). New York: IEEE Computer Society Press, 2001. 107-113.
4. Tsai CF, Lin YC, Chen CP. A new fast algorithms for mining association rules in large databases. In: Kamel AE, Mellouli K, Borne P, eds. Proc. of the 2002 IEEE Int'l Conf. on Systems, Man and Cybernetics (SMC 2002). IEEE Computer Society Press, 2002. 251-256.
5. Park J S，Chen M S，Yu P S．An effective hash-based algorithm for mining association rules[C]. Proceedings of the ACM SIGMOD．San Jose．1995：175-186.
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