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Hybrid self organizing map for overlapping clusters

Md. Sap, Mohd. Noor and Mohebi, Ehsan (2008) Hybrid self organizing map for overlapping clusters. International Journal of Signal Processing, Image Processing and Pattern Recognition, 1 (1). pp. 11-20. ISSN 2005-4254

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Official URL: http://www.sersc.org/journals/IJSIP/vol1_no1/paper...

Abstract

The Kohonen self organizing map is an excellent tool in exploratory phase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently researchers found that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the uncertainty, a two-level clustering algorithm based on SOM which employs the rough set theory is proposed. The two-level stage Rough SOM (first using SOM to produce the prototypes that are then clustered in the second stage) is found to perform well and more accurate compared with the proposed crisp clustering method (Incremental SOM) and reduces the errors.

Item Type:Article
Uncontrolled Keywords:clustering, rough set, SOM, uncertainty, incremental
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:8945
Deposited By: Ms Zalinda Shuratman
Deposited On:09 Jun 2009 09:09
Last Modified:20 Oct 2010 09:31

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