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An New Initialization Method for Fuzzy c-Means Algorithm Based on Density

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Fuzzy Information and Engineering

Part of the book series: Advances in Soft Computing ((AINSC,volume 54))

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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© 2009 Springer-Verlag Berlin Heidelberg

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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

  • eBook Packages: EngineeringEngineering (R0)

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