Abstract
In this paper an optimization method for fuzzy c-means algorithm based on grid and density is proposed in order to solve the problem of initializing fuzzy c-means algorithm. Grid and density are needed to extract approximate clustering center from sample space. Then, an optimization method for fuzzy c-means algorithm is proposed by using amount of approximate clustering centers to initialize classification number, and using approximate clustering centers to initialize initial clustering centers. Experiment shows that this method can improve clustering result and shorten clustering time validly.
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© 2007 Springer-Verlag Berlin Heidelberg
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Kaiqi, Z., guannan, D., Xiaoyan, K. (2007). An Optimization Method for Fuzzy c-Means Algorithm Based on Grid and Density. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_37
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DOI: https://doi.org/10.1007/978-3-540-71441-5_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71440-8
Online ISBN: 978-3-540-71441-5
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