ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

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.

[img] PDF
Model-theoretic_approach.pdf - Published Version
Restricted to Registered users only

Download (728Kb) | Request a copy
Official URL: http://dx.doi.org/10.1016/0950-7051(91)90012-Q

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
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
URI: http://eprints.iisc.ernet.in/id/eprint/33990

Actions (login required)

View Item View Item