Earticle

초록

영어

Selecting the initial clustering centers randomly will cause an instability final result, and make it easy to fall into local minimum. To improve the shortcoming of the existing kmeans clustering center selection algorithm, an optimized k-means algorithm for selecting initial clustering centers is proposed in this paper. When the number of the sample’s maximum density parameter value is not unique, the distance between the plurality samples with maximum density parameter values is calculated and compared with the average distance of the whole sample sets. The k optimized initial clustering centers are selected by combing the algorithm proposed in this paper with maximum distance means. The algorithm proposed in this paper is tested through the UCI dataset. The experimental results show the superiority of the proposed algorithm.

목차

Abstract
 1. Introduction
 2. The k-means Algorithm based on the Distribution of Maximum Density Points
 3. The Optimized k-means Algorithm for Selecting Initial Clustering Center
 4. Experimental Results and Analysis
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Jianhui Song [ School of Automation and Electrical Engineering, Shenyang ligong University, Shenyang, Liaoning, 110159, P.R. China ]
• Xuefei Li [ School of Automation and Electrical Engineering, Shenyang ligong University, Shenyang, Liaoning, 110159, P.R. China ]
• Yanju Liu [ School of Automation and Electrical Engineering, Shenyang ligong University, Shenyang, Liaoning, 110159, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605