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Extracting compact and information lossless sets of fuzzy association rules

机译：提取紧凑且信息无损的模糊关联规则

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摘要

Applying classical association rule extraction framework on fuzzy datasets leads to an unmanageably highly sized association rule sets. Moreover, the discretization operation leads to information loss and constitutes a hamper towards an efficient exploitation of the mined knowledge. To overcome such a drawback, this paper proposes the extraction and the exploitation of compact and informative generic basis of fuzzy association rules. The presented approach relies on the extension, within the fuzzy context, of the notion of closure and Galois connection, that we introduce in this paper. In order to select without loss of information a generic subset of all fuzzy association rules, we define three fuzzy generic basis from which remaining (redundant) FARs are generated. This generic basis constitutes a compact nucleus of fuzzy association rules, from which it is possible to informatively derive all the remaining rules. In order to ensure a sound and complete derivation process, we introduce an axiomatic system allowing the complete derivation of all the redundant rules. The results obtained from experiments carried out on benchmark datasets are very encouraging. They highlight a very important reduction of the number of the extracted fuzzy association rules without information loss.
机译：将经典关联规则提取框架应用于模糊数据集会导致难以管理的高关联规则集。此外，离散化操作导致信息丢失，并且妨碍了对挖掘的知识的有效利用。为了克服这种缺陷，本文提出了模糊关联规则的紧凑，信息丰富的通用基础的提取和开发。所提出的方法依赖于在本文中介绍的模糊和Galois连接的概念在模糊上下文中的扩展。为了在不丢失信息的情况下选择所有模糊关联规则的通用子集，我们定义了三个模糊通用基础，从中可以生成剩余的（冗余）FAR。这种通用基础构成了模糊关联规则的紧密核心，从中可以有信息地得出所有其余规则。为了确保完善和完整的推导过程，我们引入了公理系统，可以完全推导所有冗余规则。从基准数据集上进行的实验获得的结果令人鼓舞。它们突出显示了非常重要的减少提取的模糊关联规则的数量而没有信息丢失的情况。

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