Mining Frequent Itemsets in Association Rule Mining Using Improved SETM Algorithm

被引：2
|
作者：
Hanirex, D. Kerana [1 ]
Kaliyamurthie, K. P. [1 ]
机构：
[1] Bharath Univ, Dept Comp Sci & Engn, Madras, Tamil Nadu, India
来源：
ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS, ICAIECES 2015 | 2016年 / 394卷
关键词：
D O I：
10.1007/978-81-322-2656-7_70
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Association rule mining is one of the recent data mining research. Mining frequent itemsets in relational databases using relational queries give great attention to researchers nowadays. This paper implements modified set oriented algorithm for mining frequent itemsets in relational databases. In this paper, the sort and merge scan algorithm SETM (Houtsma and Swami, IEEE 25-33 (1995)) [1] is implemented for super market data set which is further improved by integrating transaction reduction technique. Our proposed algorithm Improved SETM (ISETM) generate the frequent itemsets from the database and find its execution time. Finally the performance of the algorithm is compared with the traditional Apriori and SETM algorithm.
引用
收藏
页码：765 / 773
页数：9
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