Some methods for processing missing data by applying data mining are introduced.

This algorithm made the missing data contributes to the support of possibly matching patterns.

Multiple imputation for analyzing 2×2 cross over design with missing data;

EM estimation for missing data in medical research;

Asymptotic behavior of semiparametric regression model under missing data;

Firstly,based on correlation analysis,five reference points which were most correlative with the data missing points were obtained,then the data of both reference points and data missing point in the stage were simulated and verified.

In order to make a complete data set,the method of linear regression was adopted to fill the imperfect data set,and the method of eigenvalue of group decision was taken to put the papers in order.

The processing of imperfect data is one of the important problems that need to be specially considered in the realization of information management system.

Dealing With Incomplete Data Based On Strategy Pattern;

In this paper,a model based on associative classification technique is presented in order to find the relationship between the load and the factors correlated with load forecasting accuracy,which can be used in incomplete data processing.

Attributeoriented knowledge discovery method is proved to be a powerful approach for mining incomplete data in the large database in our experiment of this paper.