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An Optimization Method for Fuzzy c-Means Algorithm Based on Grid and Density

  • Conference paper
Fuzzy Information and Engineering

Part of the book series: Advances in Soft Computing ((AINSC,volume 40))

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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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References

  1. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)

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  2. Chen, Z., Meng, Q., Wei, Z., et al.: A Fast Clustering Algorithm Based on Grid and Density Condensation Point. Journal of Harbin Institute of Technology 37, 1654–1657 (2005)

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  3. Deng, G., Zou, K.: Optimizing the structure of clustering neural networks based on grid and density. Journal of Harbin Engineering University 27, 55–58 (2006)

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  4. Hu, Y., Chen, G.: An Effective Cluster Analysis Algorithm Based on Grid and Intensity. Computer Applications 23, 64–67 (2003)

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Author information

Authors and Affiliations

  1. College of Information Engineering, Dalian University, University Key Lab of Information Sciences and Engineering, Dalian University, Dalian 116622, China

    Zou Kaiqi, Deng guannan & Kong Xiaoyan

Authors
  1. Zou Kaiqi
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  2. Deng guannan
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  3. Kong Xiaoyan
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Editor information

Bing-Yuan Cao

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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

  • eBook Packages: EngineeringEngineering (R0)

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