Sridhar, V and Murty, Narasimha M (1991) Model-theoretic approach to clustering. In: Knowledge-Based Systems, 4 (2). pp. 87-94.
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The paper deals with a model-theoretic approach to clustering. The approach can be used to generate cluster description based on knowledge alone. Such a process of generating descriptions would be extremely useful in clustering partially specified objects. A natural byproduct of the proposed approach is that missing values of attributes of an object can be estimated with ease in a meaningful fashion. An important feature of the approach is that noisy objects can be detected effectively, leading to the formation of natural groups. The proposed algorithm is applied to a library database consisting of a collection of books.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier science.|
|Keywords:||Model theory;clustering;knowledge-based clustering;maximal model;noisy data;natural clusters;disjunctive cluster description.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||24 Nov 2010 09:23|
|Last Modified:||24 Nov 2010 09:23|
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