An automated entity-relationship clustering algorithm for conceptual database design

Entity–relationship (ER) modeling is a widely accepted technique for conceptual database design. However, the complexities inherent in large ER diagrams have restricted the effectiveness of their use in practice. It is often difficult for end-users, or even for well-trained database engineers and designers, to fully understand and properly manage large ER diagrams. Hence, to improve their understandability and manageability, large ER diagrams need to be decomposed into smaller modules by clustering closely related entities and relationships. Previous researchers have proposed many manual and semi-automatic approaches for such clustering. However, most of them call for intuitive and subjective judgment from ‘‘experts’’ at various stages of their implementation. We present a fully automated algorithm that eliminates the need for subjective human judgment. In addition to improving their understandability and manageability, an automated algorithm facilitates the re-clustering of ER diagrams as they undergo many changes during their design, development, and