Concept-Based Data Classification in Relational Databases

Data classification is a process which groups objects with common properties into classes and produces a classification scheme over a set of data objects. Data classification is useful for understanding and organizing database data and building hierarchical schemes in databases. We investigate data classification in relational databases and develop a method for data classification by concept-based generalization. Our method applies an attribute-oriented generalization technique which utilizes the knowledge about data concepts, integrates a data classification process with relational operations, and provides an efficient way for classification of data in relational databases. The characteristics of each class can be extracted automatically in the classification process. Moreover, quantitative information can be registered in the generalization process to assist the classification of data based on database statistics. Our analysis of the classification algorithms shows that the attribute-oriented approach substantially reduces the complexity of data classification in large databases.