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An Improved Parallel K-Means Algorithm Based on Cloud Computing

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Book cover Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

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Abstract

In this paper we presented CK-means clustering algorithm based on improved K-means algorithm and the Canopy algorithm, which uses MapReduce programming model of Hadoop platform. The experimental results prove that the CK-means algorithm has a good advantage in the processing of large data sets, in the acceleration ratio, accuracy, expansion rate, and the effect of the algorithm after deploying on the Hadoop clusters.

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Acknowledgments

This research is supported by the Fundamental Research Funds for the Central Universities (No. 2015ZM039) and scientific research project for Guangdong University of Science and Technology (No. GKY-2014KYYB-10).

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Authors and Affiliations

  1. Department of Computer Science, Guangdong University of Science and Technology, Dongguan, China

    Dongbo Zhang

  2. Guangzhou Institute of Modern Industrial Technology, South China University of Technology, Guangzhou, China

    Yanfang Shou

Authors
  1. Dongbo Zhang
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  2. Yanfang Shou
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Corresponding author

Correspondence to Dongbo Zhang .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

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© 2016 Springer Science+Business Media Singapore

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Zhang, D., Shou, Y. (2016). An Improved Parallel K-Means Algorithm Based on Cloud Computing. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_32

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  • DOI: https://doi.org/10.1007/978-981-10-0356-1_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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