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An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing

Md. Sap, Mohd. Noor and Mohebi, Ehsan (2009) An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing. In: Lecture Notes in Business Information Processing. Springer Verlag, Germany, pp. 389-401. ISBN 978-364201346-1

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Official URL: http://dx.doi.org/10.1007/978-3-642-01347-8_33

Abstract

One of the popular tools in the exploratory phase of Data mining and Pattern Recognition is the Kohonen Self Organizing Map (SOM). The SOM maps the input space into a 2-dimensional grid and forms clusters. Recently experiments represented that to catch the ambiguity involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the ambiguity involved in cluster analysis, a combination of Rough set Theory and Simulated Annealing is proposed that has been applied on the output grid of SOM. Experiments show that the proposed two-stage algorithm, first using SOM to produce the prototypes then applying rough set and SA in the second stage in order to assign the overlapped data to true clusters they belong to, outperforms the proposed crisp clustering algorithms (i.e. I-SOM) and reduces the errors.

Item Type:Book Section
Additional Information:11th International Conference on Enterprise Information Systems, ICEIS 2009; Milan; 6 May 2009 through 10 May 2009; ISSN : 18651348
Uncontrolled Keywords:ambiguity, clustering, rough set, self organizing map, simulated annealing
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:12991
Deposited By: Ms Zalinda Shuratman
Deposited On:12 Jul 2011 01:31
Last Modified:12 Jul 2011 01:31

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