Knowledge-based clustering approach for data abstraction

Clustering techniques have been used for data abstraction. Dara abstraction has many applications in the contect of data-bases. Conceptual models are used to bridge the gap between the user's view of a database and the physical view of the database. Semantic models evolved to overcome the limitations of classical data models such as network and relational models. The paper uses a knowledge-based clustering algorithm to extend the abstractions, such as classification and association, which are employed in the semantic modeling of databases. The complexity of the proposed clustering algorithm is analysed. The extended semantic model can be used to design databases in which useful and interesting queries can be answered. The efficacy of the proposed knowledge-based clustering approach is examined in the context of a library database.