Consideration of Detecting Data and Functional Dependency in Tabular Data with Missing Values by the Obtained Rules

Functional dependency is an essential concept between attributes in relational algebra and tabular data analysis, and it is applied to the decomposition of tabular data. On the other hand, data dependency is a concept between attribute values. Functional dependency is usually given, and data dependency is often recognized after data analysis. We may recognize the hidden functional dependency as a particular case of data dependency. In this paper, we apply the rules obtained by the NIS-Apriori-based rule generator, which was implemented to handle rules from tabular and tabular data with missing values. We detect some candidates CONs of condition attributes that affect the decision attribute Dec using the obtained rules. We then apply the same rule generator specifying the detected one CON to determine the actual degree of dependency. This step eliminates the need to enumerate all CONs to understand dependencies and can handle extended dependencies for DIS and NIS. A running example using the implemented tools is also provided.