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The International Conference on Cyber Security Intelligence and Analytics

CSIA 2020: Cyber Security Intelligence and Analytics pp 310–316Cite as

K-Means Algorithm Based on Initial Cluster Center Optimization

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1146))

Abstract

K-means algorithm is a kind of algorithm with higher frequency at present, but with the continuous research, this algorithm gradually exposes some defects in practical application, which is manifested in the choice of random and sensitive noise points, which is difficult. Get accurate and reliable clustering solutions. Aiming at this kind of defect, a K-means algorithm based on the distance density of noise points is proposed. Specifically, it uses the effect of density and distance on the cluster center to perform weighted analysis and prediction of the randomness of its data. Processing, based on the determination of data weights, further introduce the “minimum and maximum principle”, which can automatically select the initial clustering center and determine the number of clustering centers. The research results show that this new algorithm can meet the expected requirements, which can not only ensure the accuracy and reliability of the optimization results from the source, but also help people to conduct a more comprehensive and rigorous analysis of the data.

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Author information

Authors and Affiliations

  1. Chongqing Information Technology Designing Co. Ltd., Chongqing, 401120, China

    Xiaolin Li

  2. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications Research Center of New Telecommunication Technology Applications, Chongqing, 400065, China

    Hang Tan

Authors
  1. Xiaolin Li
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  2. Hang Tan
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Corresponding author

Correspondence to Hang Tan .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Zheng Xu

  2. Department of Software Engineering and Game Development, Kennesaw State University, Marietta, GA, USA

    Reza M. Parizi

  3. Manchester Metropolitan University, Stockport, UK

    Mohammad Hammoudeh

  4. School of Engineering and Sciences, Tecnologico de Monterrey, Puebla, Mexico

    Octavio Loyola-González

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Cite this paper

Li, X., Tan, H. (2020). K-Means Algorithm Based on Initial Cluster Center Optimization. In: Xu, Z., Parizi, R., Hammoudeh, M., Loyola-González, O. (eds) Cyber Security Intelligence and Analytics. CSIA 2020. Advances in Intelligent Systems and Computing, vol 1146. Springer, Cham. https://doi.org/10.1007/978-3-030-43306-2_44

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