Earticle

초록

영어

Invasive weed optimization (IWO) is a swarm optimization algorithm with both explorative and exploitive power where the diverisity of the population is obtained by allowing the reproduction and mutation of individuals with poor fitness .Differential optimization algorithm is a random parallel algorithm according to a vector change that can make individuals change toward outstanding individuals with global convergence. For k-means algorithm , the traditional algorirhm is prone to get stuck at local optimum and is sensitive to random initialization. Based on the aforementiond background a novel optimization algorithm based hybriding DE and IWO which denoted IWODE-KM is employed to optimize the parameters of k-means and is further applied to chinese text clustering. Experiment results shows that the proposed method outperforms both of its ancestors.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. IWO Algorithm
  2.2. DE Algorithm
  2.3. Text Technology
 3. IWODE-KM Text Clustering Algorithm
  3.1. Algorithm Description
  3.2. Fitness Function
  3.3. Coding Scheme
  3.4. IWODE-KM Algorithmic Process
 4. Experimental
  4.1. Text Processing
  4.2. Results Evaluation Methods
  4.3. Experimental Setup and the Results Analysis
 5. Conclusion
 References

키워드

저자

• Lipeng YANG [ China Academy of Railway Science, Bejing, China ]
• Fuzhang WANG [ China Academy of Railway Science, Bejing, China ]
• Chunmei FAN [ China Rails Travel Technology, Co. Ltd.,Beijing, 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