基于逻辑运算的关联规则挖掘算法
首发时间:2008-09-17
摘要:为了克服传统数据挖掘频繁模式下算法迭代产生大量冗余子表,引起算法执行时间长和储存空间开销大等问题,本文提出了一种高效挖掘数据的频繁项集模式的算法FIA。该算法采用一种逻辑符号来表示数据,在仅扫描数据库一次之后,建立起二进制转换表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。通过分析表明,FAI算法比Apriori算法更有效。
关键词: 数据挖掘 逻辑运算 关联规则 频繁项集 上三角频繁矩阵
For information in English, please click here
The Algorithms of Association Rules Based on Logical Operation
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 Itemset Algorithm) is proposed. The FIA algorithm adopts a logical 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 conversion table and upper triangular frequent matrix, according to the two to produce a set of frequently. Thereby effectively narrowing the search space, speed up the processing speed. Through analysis showed that, FAI algorithm more effective than Apriori algorithm.
Keywords: Data Mining Logical Operation Association Rule Frequent Itemset Upper Triangular Frequent Matrix
基金:
论文图表:
引用
No.2410816288312216****
同行评议
共计0人参与
勘误表
基于逻辑运算的关联规则挖掘算法
评论
全部评论0/1000