Adaptive-Clustering Based Method to Estimate Null Values in Relational Databases
Data preprocessing is an essential step of knowledge discovery. Data pre-processing comprises data cleaning, data integration, data transformation, data reduction and data discretization. Estimating null values is a task of data cleaning. Null values in a database are significant sources of poor data quality. Therefore, the appropriate handling of null values is an important task of data preprocessing in relational databases. The authors propose a new method that uses adaptive learning techniques, based on clustering, to resolve the issue of null values in relational database systems.