王文正, 王文平, 许映秋, 谈英姿. 一种基于上三角频繁项集矩阵的频繁模式挖掘算法[J]. 微电子学与计算机, 2010, 27(9): 138-143.
引用本文: 王文正, 王文平, 许映秋, 谈英姿. 一种基于上三角频繁项集矩阵的频繁模式挖掘算法[J]. 微电子学与计算机, 2010, 27(9): 138-143.
WANG Wen-zheng, WANG Wen-ping, XU Ying-qiu, TAN Ying-zi. Algorithms for Mining Frequent Itemsets Based on Upper Triangular Frequent Matrix[J]. Microelectronics & Computer, 2010, 27(9): 138-143.
Citation: WANG Wen-zheng, WANG Wen-ping, XU Ying-qiu, TAN Ying-zi. Algorithms for Mining Frequent Itemsets Based on Upper Triangular Frequent Matrix[J]. Microelectronics & Computer, 2010, 27(9): 138-143.

一种基于上三角频繁项集矩阵的频繁模式挖掘算法

Algorithms for Mining Frequent Itemsets Based on Upper Triangular Frequent Matrix

  • 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.

     

    Abstract: 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 Iternset 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.

     

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