Model-theoretic approach to clustering

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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Abstract

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
Publication:	Knowledge-Based Systems
Publisher:	Elsevier science
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
Date Deposited:	24 Nov 2010 09:23
Last Modified:	24 Nov 2010 09:23
URI:	http://eprints.iisc.ac.in/id/eprint/33990

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