四测度两论域上的粒关联规则
Granular association rules on two universes with four measures
摘要：
关联规则揭示隐藏在多个表中的模式。现有的规则通常通过两个衡量标准进行评估，即支持度和信任度。然而，这两个指标可能不足以描述规则的强度。在本文中，我们介绍了粒度关联规则的四个措施，揭示了两个宇宙中的颗粒之间的连接，并提出了三个算法的规则挖掘。例如，“40%的男性喜欢至少30%的酒; 45%的顾客是男性，6%的产品是酒。这里45%、6%、40%和30%分别是源覆盖率、目标覆盖率、源置信度和目标置信度。通过这些措施，我们的规则比现有的规则在语义上更丰富。通过考虑对源/目标置信度的特殊要求，得到了三种规则子类型。然后，我们定义了一个规则挖掘问题，并设计了一个三明治算法，不同的子类型的规则检查方法。在真实世界数据集上的实验表明，专用于三种亚型的方法比一般情况下的方法快2-3个数量级。针对一个特定子类型的前向算法和后向算法可以进一步加快挖掘过程。本文的工作开辟了关系关联规则挖掘、粒计算和粗糙集的新的研究方向。
英文摘要：
Relational association rules reveal patterns hide in multiple tables. Existing rules are usually evaluated through two measures, namely support and confidence. However, these two measures may not be enough to describe the strength of a rule. In this paper, we introduce granular association rules with four measures to reveal connections between granules in two universes, and propose three algorithms for rule mining. An example of such a rule might be "40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol." Here 45%, 6%, 40%, and 30% are the source coverage, the target coverage, the source confidence, and the target confidence, respectively. With these measures, our rules are semantically richer than existing ones. Three subtypes of rules are obtained through considering special requirements on the source/target confidence. Then we define a rule mining problem, and design a sandwich algorithm with different rule checking approaches for different subtypes. Experiments on a real world dataset show that the approaches dedicated to three subtypes are 2-3 orders of magnitudes faster than the one for the general case. A forward algorithm and a backward algorithm for one particular subtype can speed up the mining process further. This work opens a new research trend concerning relational association rule mining, granular computing and rough sets.
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