Abstract
In this paper an initialization method for fuzzy c-means (FCM) algorithm is proposed in order to solve the two problems of clustering performance affected by initial cluster centers and lower computation speed for FCM. Grid and density are needed to determine the number of clusters and the initial cluster centers automatically. Experiment shows that this method can improve clustering result and shorten clustering time validly.
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Zou, Kq., Wang, Zp., Pei, Sj., Hu, M. (2009). An New Initialization Method for Fuzzy c-Means Algorithm Based on Density. In: Cao, By., Zhang, Cy., Li, Tf. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88914-4_68
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DOI: https://doi.org/10.1007/978-3-540-88914-4_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88913-7
Online ISBN: 978-3-540-88914-4
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