Abstract: 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.

Key words: Relational data, Approximate functional dependence, Attribute weight, Minimal trivial functional dependence