An Improved DFD Based on Attribute Partition Information Entropy
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Liu Bohong, Jiang Xinyuan
DFD is a depth-traversal functional dependencies discovery method, it does not consider association between nodes of power set lattice. We improved DFD by using attribute information entropy combined with DFD to reduce the repeated frequencies of traversals. Datasets of UCI are used to verify that the improved DFD runs faster than original DFD.
Functional Dependencies, Attribute Partition, Information Entropy.