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K-Means Algorithm Based on Initial Cluster Center Optimization

  • Xiaolin Li
  • Hang TanEmail author
Conference paper
  • 17 Downloads
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

K-means algorithm Distance density Cluster center 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Chongqing Information Technology Designing Co. Ltd.ChongqingChina
  2. 2.School of Communication and Information EngineeringChongqing University of Posts and Telecommunications Research Center of New Telecommunication Technology ApplicationsChongqingChina

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