预期关联规则集及其基数的定量分析  

Expecting Association Rule Set and Quantitative Analysis for Its Base Number

作　　者：李凯里[1] 王立宏[1] 童向荣[1] 

机构地区：[1]烟台大学计算机学院,烟台264005

出　　处：《模式识别与人工智能》2010年第3期402-407,共6页Pattern Recognition and Artificial Intelligence

摘　　要：给定数据库,在不考虑支持度和可信度情况下,事先能否预知最终会挖掘出多少条关联规则,这是个值得研究的问题.为此文中提出预期关联规则的概念,使上述问题转化成为如何计算预期关联规则集基数的问题.分别给出布尔型和数量型两种情况下的计算公式.对于数量型数据集,讨论当转换为布尔型数据后各个项集元素呈现的互斥性质.利用此性质导出一个膨胀矩阵和膨胀算法.该方法相对简洁地解决数量型数据集预期关联规则集基数的计算问题.计算和测试结果都表明,预期关联规则总量随着互斥元素的增加呈现下降趋势.这些结果对于深刻理解关联规则挖掘的实质,进而研发更加高效的挖掘算法十分有益.It is deserved to foresee how many association rules will be mined from a given database without taking support and confidence in consideration. Hence the concept of expecting association rule is proposed in this paper to turn the above problem into how to calculate the base number of an expecting association rule set. The categorical and continuous computing formulas are presented respectively. The exclusive property of items in itemset is discussed after the transformation of continuous data into categorical data. An expanding matrix and an expanding method is deduced by the exclusive property. This method is used to calculate the base number of an expecting association rule set of continuous dataset in a brief way. The analysis and test results show that the size of an expecting association rule set decrease as the amount of exclusive items increase. These results are helpful to understand the essence of association rule mining and furtherly develop more highly efficient mining algorithm.

关 键 词：关联规则 预期关联规则 0支持度 膨胀算法 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]