Earticle

초록

영어

Kernel K-means clustering (KKC) is an effective nonlinear extension of K-means clustering, where all the samples in the initial space are mapped into the feature space and then K-means clustering is performed based on the mapped data. However, all the mapped data are expressed by the implicit form, which causes the initial cluster centers can’t be selected flexibly. Once the selected initial cluster centers aren’t suitable, it tends to fall into local optimal solutions and can’t guarantee stable result. Based on a standard orthogonal basis of the sub-space spanned by all the mapped data, a novel improving non-linear algorithm of KKC is presented in this paper. The novel algorithm can express the mapped data using the explicit form, which make it very flexible to select the initial cluster centers as the linear K-means clustering does. Moreover, the computational complexity of the presented algorithm is also significantly reduced compared to that of KKC. The results of simulation experiments illustrate the proposed method can eliminate the sensitivity to the initial cluster centers and simplify computational processing.

목차

Abstract
 1. Introduction
 2. Kernel K-Means Clustering (KKC)
 3. The Optimizing Algorithm of KKC (OKKC)
  3.1. Second-Order Headings
  3.2 How to Get a Standard Orthogonal Basis
 4. Experiments
 5. Conclusion
 References

키워드

저자

• Chen Ning [ Mechanical and Energy Engineering College, Jimei University ]
• Zhang Hongyi [ School of Optoelectronic & Communication Engineering, Xiamen University of Technology ]

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