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A Novel on Automatic K Value for Efficiency Improvement of K-means Clustering

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Advanced Multimedia and Ubiquitous Engineering (FutureTech 2017, MUE 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 448))

Abstract

The development of H/W and S/W has shortened the repetition cycle of new data generation and produced various categories of data. Machine learning, in particular, attracts explosive interest as it categorizes and analyzes data through artificial intelligence and contests against man. Once generated, data have their importance highlighted in terms of utilization. It is critical to analyze the data from the past and cluster new data for the utilization of data. The present study thus investigated an algorithm of determining the initial number of clusters automatically, which is part of problems with the K-means algorithm used in data clustering. The study also proposed an approach of optimizing the number of clusters through principal component analysis, a pre-processing process, with the input data for clustering. Its performance evaluation results show the accuracy rate of 87.6% or so.

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Acknowledgments

The research was supported by ‘Area Software Convergence Commercialization Program’, through the Ministry of Science, ICT and Future Planning (S0417161012).

Author information

Authors and Affiliations

  1. Department of Multimedia Engineering, GwangYang SW Convergence Institute, Sunchon National University, Suncheon, Korea

    Se-Hoon Jung

  2. Research and Development Team, GwangYang SW Convergence Institute, Suncheon, Korea

    Kyoung-Jong Kim

  3. Harvard Medical School, Boston, USA

    Eun-Cheon Lim

  4. School of Information Communication and Multimedia Engineering, Sunchon National University, Suncheon, Korea

    Chun-Bo Sim

Authors
  1. Se-Hoon Jung
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  2. Kyoung-Jong Kim
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  3. Eun-Cheon Lim
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  4. Chun-Bo Sim
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Corresponding author

Correspondence to Chun-Bo Sim .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    James J. (Jong Hyuk) Park

  2. School of Computing and Information Sciences, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  3. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, Adelaide, Australia

    Kim-Kwang Raymond Choo

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© 2017 Springer Nature Singapore Pte Ltd.

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Jung, SH., Kim, KJ., Lim, EC., Sim, CB. (2017). A Novel on Automatic K Value for Efficiency Improvement of K-means Clustering. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_31

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  • DOI: https://doi.org/10.1007/978-981-10-5041-1_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5040-4

  • Online ISBN: 978-981-10-5041-1

  • eBook Packages: EngineeringEngineering (R0)

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