A novel clustering algorithm based on the extension theory and genetic algorithm

Authors:
Meng-Hui Wang;Yi-Feng Tseng;Hung-Cheng Chen;Kuei-Hsiang Chao
Affiliations:
Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan;Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan;Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan;Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This paper presents a novel clustering method this is called extension genetic algorithm (EGA). The new method is a combination of extension theory and genetic algorithm (GA). In the past, we used the extension method in some clustering problems. With the method, we had to rely on experiences to set rules on classical domain and weight, which caused to increase two tedious and complicated steps in clustering processes. In order to improve this defect, the paper uses the EGA to find the best parameter of classical domain. Through the simulations, we prove that this new method can eliminate try and error adjustment of modeling parameters and increase the accuracy of clustering problems. Experimental results from three different examples, including two benchmark data sets and one practical application, verify the effectiveness and applicability of the proposed work.