Abstract
This article proposes a fuzzy C-means clustering analysis method, which is based on DNA genetic algorithm. DNA encoding is used to analyze the center of the cluster and the quality of clustering is judged by eigenvectors and the sum of Euclidean distance of the corresponding cluster center. Through selection, crossover, mutation and inversion operation the encoding of cluster centers can be optimized, thus to get the best cluster center of cluster division. According to the simulation results the effect of this method is superior to the genetic algorithm of fuzzy C-means clustering analysis.
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Zang, W., Liu, X., Wang, Y. (2013). Clustering Analysis Research Based on DNA Genetic Algorithm. In: Zu, Q., Hu, B., Elçi, A. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2012. Lecture Notes in Computer Science, vol 7719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37015-1_70
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DOI: https://doi.org/10.1007/978-3-642-37015-1_70
Publisher Name: Springer, Berlin, Heidelberg
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