Earticle

초록

영어

Aiming at the problem of traditional fuzzy C-means clustering algorithm that it is sensitive to the initial clustering centers and easy to fall into the local optimization, an improved algorithm that combines Improved Quantum Genetic Optimization with FCM algorithm is proposed. In this study, chromosomes are comprised of quantum bits encoded by real number. Chromosomes are renovated by quantum rotating gates and mutated by quantum hadamard gate. The gradients of object function are utilized in adjusting the value of rotating angle by a dynamic strategy. Each chain of genes represents a optimization result, Therefore, a double searching space is acquired for the same number of chromosomes. Experimental results show that the proposed method improves the stability and the accuracy of classification．

목차

Abstract
 1. Introduction
 2. Fuzzy Clustering
  2.1. Fuzzy C-Means
 3. Quantum Optimization Algorithm
  3.1. Quantum Bit
  3.2. Quantum Chromosome Encoding
 4. Fuzzy Clustering Algorithm based on Improved Quantum Genetic Optimization
  4.1. Quantum Coding and the Solution Space Transformation
  4.2. Quantum Revolve Gate
  4.3. Quantum Mutation
  4.4. Fitness Function
  4.5. Procedure of IQGA
 5. Experimental Simulations and Analysis
  5.1. Experimental Data Set
  5.2. Experimental Testing and Results Analysis
 6. Conclusion
 References

키워드

저자

• An-Xin Ye [ Xingzhi College Zhejiang Normal University, Jinhua 321002, Zhejiang, China ]
• Yong-Xian Jin [ College of Mathematics; Physics and Information Engineering; Zhejiang Normal University, Jinhua 321002, Zhejiang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605