Earticle

초록

영어

K-means algorithm is sensitive to initial cluster centers and its solutions are apt to be trapped in local optimums. In order to solve these problems, we propose an optimized ar-tificial bee colony algorithm for clustering. The proposed method first obtains optimized sources by improving the selection of the initial clustering centers; then, uses a novel dy-namic local optimization strategy utilizing roulette wheel selection algorithm for further enhancing local optimization. To prove its effectiveness, we validate the proposed algo-rithm on four datasets from UCI and compared the results with K-means, K-means++ and Artificial Bee Colony algorithm. Experiment results show that the proposed algo-rithm performs better than other clustering algorithms.

목차

Abstract
 1. Introduction
 2. Foundations
  2.1. K-means Algorithm
  2.2. Artificial Bee Colony Algorithm
 3. An Optimized Artificial Bee Colony Algorithm for Clustering
  3.1. Optimize The Initial Food Sources
  3.2. Dynamic Local Optimization Strategies
  3.3. Algorithm Description
 4. Experimental Results
 5. Conclusions
 Acknowledgment
 References

키워드

저자

• An Gong [ School of Computer & Communication Engineering, China University of Petrole-um, Qingdao 266580, China ] Corresponding Author
• Yun Gao [ School of Computer & Communication Engineering, China University of Petrole-um, Qingdao 266580, China ]
• Xingmin Ma [ School of Computer & Communication Engineering, China University of Petrole-um, Qingdao 266580, China ]
• Wenjuan Gong [ School of Computer & Communication Engineering, China University of Petrole-um, Qingdao 266580, China ]
• Huayu Li [ School of Computer & Communication Engineering, China University of Petrole-um, Qingdao 266580, China ]
• Zhen Gao [ School of Computer & Communication Engineering, China University of Petrole-um, Qingdao 266580, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605