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International Conference on Swarm Intelligence

ICSI 2016: Advances in Swarm Intelligence pp 107–114Cite as

An Improved K-means Clustering Algorithm Based on the Voronoi Diagram Method

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9713))

Abstract

To solve the problem that the K-means clustering algorithm is over dependent on the K value and the clustering center, we proposed an improved K-means clustering algorithm, VK-means algorithm in this paper. In the initial stage, the Voronoi diagram was adapted in the K-means algorithm to get a better K value and clustering center. By means of weighted average of K-means algorithm, the results of the criterion function is improved. This method could fast convergence and improve performance of the algorithm. The superiority of the improved algorithm was verified by experiments on Weka platform.

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References

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Acknowledgments

This work is supported by National Nature Science Foundation of China (Grant No. 61462058) and Lanzhou Science and technology project (2014-1-127).

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Lanzhou Jiaotong University, 730070, Lanzhou, China

    Jiuyuan Huo & Honglei Zhang

Authors
  1. Jiuyuan Huo
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  2. Honglei Zhang
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Corresponding author

Correspondence to Jiuyuan Huo .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzen University, Shenzhen, China

    Li Li

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Huo, J., Zhang, H. (2016). An Improved K-means Clustering Algorithm Based on the Voronoi Diagram Method. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-41009-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41008-1

  • Online ISBN: 978-3-319-41009-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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