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An Enhancing K-Means Algorithm Based on Sorting and Partition

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Information Computing and Applications (ICICA 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 391))

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Abstract

The accuracy and efficiency as the two main evaluation indexes for k-means algorithm are influenced by the choice of initial clustering centers and the partition method of data points. In this paper, in view of the deficiency of direct k-means algorithm which chooses initial centers randomly, we propose a novel method about initial clustering centers based on sorting and partition and apply it to real data as well as simulated data, which show that this is an efficient method to improve the clustering accuracy and efficiency.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Soochow University, 215006, Suzhou, China

    Yin Jun-wei, Chen Jian-ming, Xue Bai-li & Zhang Jian

Authors
  1. Yin Jun-wei
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  2. Chen Jian-ming
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  3. Xue Bai-li
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  4. Zhang Jian
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Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, 800 Dongchuan Road, Dianxinqunlou 1-401, 200240, Shanghai, China

    Yuhang Yang

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore, Singapore

    Maode Ma

  3. College of Science, Hebei United University, 063009, Tangshan, Hebei, China

    Baoxiang Liu

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Jun-wei, Y., Jian-ming, C., Bai-li, X., Jian, Z. (2013). An Enhancing K-Means Algorithm Based on Sorting and Partition. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53932-9_36

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  • DOI: https://doi.org/10.1007/978-3-642-53932-9_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53931-2

  • Online ISBN: 978-3-642-53932-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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