Earticle

초록

영어

The traditional K-Means clustering is sensitive to random selection of initial cluster centroids, easily into the local optimal solution. In this paper, an efficient aggregation algorithm which combined with Artificial bee colony and K-Means algorithm is proposed to apply to the diagnosis of liver function. The algorithm reduced the dependence on the initial cluster centroids and the probability to be trapped by local optimal solution, thus assigning data points to their appropriate cluster more efficient. The experimental results show that algorithm proposed in this paper is superior to the K-Means clustering in diagnosis of liver function.

목차

Abstract
 1. Introduction
 2. Computer-aided Diagnosis of Liver Function
 3. Aggregation Algorithm Combined with ABC and K-Means Clustering
  3.1. Artificial Bee Colony Algorithm
  3.2. K-Means Clustering Algorithm
  3.3. Aggregation Algorithm
 4. Experiments
 5. Conclusion
 References

키워드

저자

• Zhang Lin [ School of Computer Science and Technology, Harbin University of Science and Technology, 150080 Harbin, China ]
• Li Peng [ School of Software, Harbin University of Science and Technology, 150080 Harbin, China, School of Computer Science and Technology, Harbin University of Science and Technology, 150080 Harbin, China ]
• Qiao Pei-li [ School of Computer Science and Technology, Harbin University of Science and Technology, 150080 Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605