基于近似函数依赖的关系数据属性权重评估方法

在现实应用中，一些关系数据的规范化程度不高，往往存在数据冗余和不一致现象。为了有效评估此类数据 中的属性重要程度，提出了一种基于近似函数依赖的属性权重评估方法。该方法基于一致集的概念导出最大集，生成 最小非平凡函数依赖集，从而找出属性之间的近似函数依赖关系，进而求出近似候选码和近似关键字，在此基础上根 据属性支持度计算属性权重。实验结果和分析表明，提出的属性权重评估方法能够合理地获取关系数据中的属性重 要程度，算法具有较好的稳定性和较高的执行效率。

Attribute Weight Evaluation Approach Based on Approximate Functional Dependencies

In real applications, the normalization of some relational data is unreasonable and thus leads to the problems of data redundancy and inconsistency. In order to automatically evaluate the attribute importance of this kind of relational data, this paper proposed an attribute weight evaluation approach based on approximate functional dependencies. Based on the concept of the agree set, the maximum set is exported, and the minimum nontrivial functional dependence sets are generated conseduently in order to find the approximate dependence relations, thus the approximate key and approxi- mate keywords can be found. After this, this approach computes the weight of each attribute according to the supported degree of attribute. The experimental results and analysis demonstrate that the attribute weight evaluation approach presented in this paper can reasonably obtain the importance of the attribute in a relation, and the algorithm is stable and has high performance as well.