Abstract
In this paper, a framework for replacing missing values in a database is proposed since a real-world database is seldom complete. Good data quality in a database can directly improve the performance of any data mining algorithm in various applications. Our proposed framework adopts the basic concepts from conditional probability theories and further develops an algorithm to facilitate the capability of handling both nominal and numerical values, which addresses the problem of the inability of handling both nominal and numerical values with a high degree of accuracy in the existing algorithms. Several experiments are conducted and the experimental results demonstrate that our framework provides a high accuracy when compared with most of the commonly used algorithms such as using the average value, using the maximum value, and using the minimum value to replace missing values.