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Rough kohonen neural network for overlapping data detection

E., M. N. M. Sap, Mohebi (2009) Rough kohonen neural network for overlapping data detection. In: Advances in Software Engineering. Springer, Berlin/ Heidelberg, pp. 183-196. ISBN 978-3-642-10241-7

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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:Book Section
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:14456
Deposited By: Siti Khairiyah Nordin
Deposited On:26 Aug 2011 08:25
Last Modified:10 Aug 2017 00:47

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