基于项集依赖的最小关联规则挖掘

传统关联规则挖掘可能会得到大量的、杂乱的规则,它们对用户来说是不相关的或不感兴趣的。提出最小关联规则集和项集强依赖关系的概念,以实现基于项集依赖的最小关联规则挖掘算法。其不仅可以避免验证某一频繁项集下的所有非空真子集是否可形成关联规则,还可以通过删除那些过于复杂、有重复信息的规则来进一步简化传统规则集合。通过最小关联规则集可推导得到大多数冗余规则的支持度和置信度,实现了传统规则集的一种近似无损表述。采用UCI机器学习库中数据集进行实验,结果表明提出的方法得到的规则数量明显减少,且规则更加简短、无重复信息,为最小关联规则挖掘提供了更好的方法。

Minimal Association Rules Mining Based on Itemset Dependency

There are excessive and messy rules produced by traditional association rule mining, many of which are not relevant to users' interest. Minimal association rules mining algorithm was represented based on the concept of minimal association rules-set and strong dependency between items. Not only avoiding checking whether every non-empty subset of one frectuent itemset can form an association rule,but also simplifying the traditional rules set by deleting those excessivcly complex and reduplicative rules. I}he support and confidence degree of most redundant rules can be derived from minimal association rules set, which achieves a nearly lossless representation of the traditional rules set. The recults based on four benchmark data sets from UCI repository show that the number of rules generated by proposed method is reduced greatly and those rules in rules set arc more briefly without reduplicative information. This provides a better way to find minimal association rules.