A Null Value Estimation Method Based on Similarity Predictions in Rough Sets

In this paper, we utilize rough set theory as a tool to deal with the problem of null-value estimation in an incomplete information system, a rating mechanism in collaborative filtering technology is introduced into this paper for the weakness of null value estimation based on similar relational algorithm (SIM-EM), such as no sparse degree process and low accuracy, and an improved null value estimation method, which based on SIM-EM is proposed from the perspective of similarity. The null value data is predicted and filled through the similarity of objects, otherwise a dual feature weight method is proposed according to the attribute´s feature in rough set, which improves the accuracy in similarity calculation, the improved algorithm is good at dealing with sparse rough set, and the accuracy and the mean absolute error is better than the original method.