Fast algorithm for mining maximal frequent itemsets

被引：2
|
作者：
Ma, Lisheng [1 ]
Deng, Huiwen [2 ]
机构：
[1] Chuzhou Univ, Dept Comp Sci & Technol, Chuzhou, Anhui, Peoples R China
[2] Southwest Univ, Res Ctr Log & Intelligence, Chongqing, Peoples R China
来源：
PROCEEDINGS OF THE FIRST INTERNATIONAL SYMPOSIUM ON DATA, PRIVACY, AND E-COMMERCE | 2007年
关键词：
D O I：
10.1109/ISDPE.2007.66
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Efficient algorithms for mining frequent itemsets are crucial for mining association rules. Most existing work focuses on mining all frequent itemsets. However since any subset of a frequent set also is frequent, it is sufficient to mine only the set of maximal frequent itemsets. In this paper, we study the performance of two existing approaches, MAFIA and FpMAX, for mining maximal frequent itemsets. We also develop an algorithm, called FMFIA. In this algorithm, we develop and integrate two techniques in order to improve the efficiency of mining maximal frequent itemsets. We also present experimental results which show that our method outperforms the existing methods MAFIA and FpMAX.
引用
收藏
页码：86 / +
页数：2
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