Missing Value Imputation Method Based on Density Clustering and Grey Relational Analysis

In the computer-aided medical diagnosis, the problem of missing attribute values in many medical data sets brings a great challenge to data mining. To solve the problem, this paper proposes a method based on density clustering and grey relational analysis. It provides an effective solution for missing medical data. The method uses the characteristic and degree of data samples dynamic relation and the existing attribute value information to impute the missing value, in order to alleviate the difficulty brought by missing data for aided medical diagnosis. By comparison with the experiment, the proposed method can effectively solve the classification problem which causes by missing medical attribute value, accurately predict the patient’s health and provide the help to doctor’s diagnosis.