Association Rules Mining is one of the most studied and widely applied fields in Data Mining. However, the discovered models usually result in a very large set of rules; so the analysis capability, from the user point of view, is diminishing. Hence, it is difficult to use the found model in order to assist decision-making process. The previous handicap is heightened in presence of redundant rules in the final set. In this work a new definition of redundancy in association rules is proposed, based on user prior knowledge. A post-processing method is developed to eliminate this kind of redundancy, using association rules known by the user. Our proposal allows to find more compact models of association rules to ease its use in the decision-making process. The developed experiments have shown reduction levels that exceed 90 percent of all generated rules, using prior knowledge always below ten percent. So, our method improves the efficiency of association rules mining and the exploitation of discovered association rules.

中文翻译：

---

减少关联规则大小的知识冗余方法

关联规则挖掘是数据挖掘中研究最多、应用最广泛的领域之一。然而，发现的模型通常会产生非常大的规则集；因此，从用户的角度来看，分析能力正在减弱。因此，很难使用找到的模型来辅助决策过程。在最终组中存在冗余规则时，先前的差点会增加。在这项工作中，基于用户先验知识，提出了关联规则冗余的新定义。开发了一种后处理方法来消除这种冗余，使用用户已知的关联规则。我们的提议允许找到更紧凑的关联规则模型，以简化其在决策过程中的使用。开发的实验表明，使用总是低于 10% 的先验知识，减少水平超过了所有生成规则的 90%。因此，我们的方法提高了关联规则挖掘的效率和发现的关联规则的利用。