摘要
提出了一种高效挖掘数据的频繁项目集模式的算法FIA.该算法采用一种二进制符号来表示数据,在仅扫描数据库一次之后,建立起二进制向量与上三角频繁项集矩阵,根据两者来产生出频繁项集.从而有效地缩小了搜索空间,加快了处理速度.通过实验表明,FIA算法比Apriori算法更有效.
The traditional algorithms for mining association frequent patterns generate conditional sub tables, which costs much runtime and memory space. To solve these problems, a new algorithm FIA （Frequent Itemset Algorithm） is proposed. The FIA algorithm adopts a binary of symbols to compress the store data. The algorithm using logic of symbols to express data in a database, which only after one scan, and establish a binary vector and upper triangular frequent matrix, according to the both to produce a set of frequently. Thereby effectively narrowing the search space, speed up the processing speed. Through analysis showed that, FIA algorithm more effective than Apriori algorithm.
出处
《微电子学与计算机》
CSCD
北大核心
2010年第9期138-143,共6页
Microelectronics & Computer
基金
国家自然科学基金项目(70571013)
教育部新世纪优秀人才支持计划项目(NECT-06-0471)
江苏省教育厅高等教育改革研究课题
关键词
数据挖掘
关联规则
逻辑运算
频繁项集
上三角频繁矩阵
data mining
association rule
logical operation
frequent itemset
upper triangular frequent matrix
