Study of Decision Table Attribute Reduction Methods Based on Rough Set

Searching core and attribute reduction is a main issue of the rough sets theory.To solve some existing shortcomings of the decision table attribute reduction algorithm,in particular,entropy-based algorithm has low efficiency for reduction of large data sets,so it proposed an improved algorithm based on the theory of rough sets.The new algorithm changed the constraint condition in searching core through using some rough sets theory.It has high efficiency and has low time complexity in searching core and attribute reduction.Experiment results show that the algorithm can find a good attribute subset.