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Rough set based clustering of the self organizing map

Mohebi, Ehsan and Sap, M. N. N. (2009) Rough set based clustering of the self organizing map. In: Proceedings - 2009 1st Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009. Institute of Electrical and Electronics Engineers, New York, 82 -85. ISBN 978-076953580-7

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Official URL: http://dx.doi.org/10.1109/ACIIDS.2009.79

Abstract

The Kohonen Self Organizing Map (SOM) is an excellent tool in exploratory phase of data mining. The SOM is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units needs to be grouped i.e., clustered. In this paper a two-level clustering based on SOM is proposed, which employs rough set theory to capture the inherent uncertainty involved in cluster analysis. The two-stage procedure (first using SOM to produce the prototypes that are then clustered in the second stage) is found to perform well when compared with crisp clustering of the data and increase the accuracy.

Item Type:Book Section
Additional Information:2009 1st Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009; Dong Hoi; 1 April 2009 through 3 April 2009
Uncontrolled Keywords:clustering, overlapped data, rough set, SOM, uncertainty
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:13092
Deposited By: Liza Porijo
Deposited On:18 Jul 2011 08:01
Last Modified:18 Jul 2011 08:01

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