Mining Association Rules Using Fast Algorithm

被引：0
|
作者：
Anandhavalli, M. [1 ]
Jain, Sandip [1 ]
Chakraborti, Abhirup [1 ]
Roy, Nayanjyoti [1 ]
Ghose, M. K. [1 ]
机构：
[1] Sikkim Manipal Inst Technol, Dept Comp Sci Engn, E Sikkim, India
来源：
2010 IEEE 2ND INTERNATIONAL ADVANCE COMPUTING CONFERENCE | 2010年
关键词：
Association Rule Mining (ARM); Frequent itemsets; Boolean vector; relational AND operation;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The most time consuming operation in Priori-like algorithms for association rule mining is the computation of the frequency of the occurrences of itemsets (called candidates) in the database. In this paper, a fast algorithm has been proposed for generating frequent itemsets without generating candidate itemsets and association rules with multiple consequents. The proposed algorithm uses Boolean vector with relational AND operation to discover frequent itemsets. Experimental results shows that combining Boolean Vector and relational AND operation results in quickly discovering of frequent itemsets and association rules as compared to general Apriori algorithm.
引用
收藏
页码：406 / 409
页数：4
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