Sridhar, V and Murty, Narasimha M (1994) Knowledge-based clustering approach for data abstraction. In: Knowledge-Based Systems, 7 (2). pp. 103-113.
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Clustering techniques have been used for data abstraction. Data abstraction has many applications in the context 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.
|Item Type:||Journal Article|
|Additional Information:||The copyright of this article belongs to Elsevier.|
|Keywords:||Association abstraction;Classification abstraction;Clustering;Database comparison;Data abstraction;Knowledge-based clustering algorithms;Incremental clustering algorithms;Order-independent clustering algorithms;Semantic models|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||07 Jul 2006|
|Last Modified:||19 Sep 2010 04:29|
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