Abstract
In 2009, we proposed a clustering algorithm called the unsupervised kernel fuzzy clustering algorithm based on simulated annealing (UKFCSA) that generalized the conventional possibilistic clustering and can be run as an unsupervised clustering algorithm. In contrast to the conventional SA based possibilistic or kernel based possibilistic clustering, it has a relatively better performance in several aspects. However, UKFCSA is still a time consuming algorithm. In this paper, an efficient global optimization technique—differential evolution algorithm (DE) is introduced to optimize the clustering model. The contrast experiments with UKFCSA illustrate the effectiveness of the proposed algorithm.
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Qu, F., Hu, Y., Yang, Y., Gu, X. (2011). Unsupervised Kernel Fuzzy Clustering Based on Differential Evolution Algorithm in Intelligent Materials System. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23756-0_31
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DOI: https://doi.org/10.1007/978-3-642-23756-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23755-3
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