负关联规则挖掘算法研究

典型的正关联规则仅考虑事务中所列举的项目.负关联规则不但要考虑事务中所包含的项目,还必须考虑事务中所不包含的项目,它包含了非常有价值的信息.然而,对于负关联规则挖掘的研究却很少,仅有的几种算法也存在一定的局限性.为此,文中提出了一种快速有效的负关联规则挖掘算法MNAR,并给出了一种基于二进制形式的支持数计算方法.理论和实验结果表明算法MNAR是有效和可行的.

Data Mining Algorithm Based on Negative Association Rules

Typical association rules consider only items enumerated in transactions,referred to as positive association rules.Negative association rules also consider the same items,but in addition,also consider negated items,i.e.,those absent in transactions.Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other.They are also convenient for associative classifiers,classifiers that build their classification model based on association rules.Indeed,data mining using such rules necessitates examination of an exponentially large search space.Despite their usefulness,very few algorithms for mine this information have been proposed to date.In this paper,a fast and efficient algorithm MNAR is presented to discover negative association rules.Meanwhile,a method for calculating the support of itemsets is proposed.Experiments show that the MNAR algorithm is effective and feasible.