Earticle

초록

영어

In recent years, several researches have been conducted on spectral clustering to classify non-linear data in various applications. Considering the effect of selecting the appropriate eigenvectors on spectral clustering performance; various methods have been proposed weighting and ranking features. However, these methods can independently evaluate the impact of each eigenvector. Nevertheless, it is possible that several eigenvectors have duplicate or inadequate information on some clusters. Thus, we have presented a new method for finding the optimal combination of eigenvectors by several different evaluation criteria. In order to detect simultaneously the optimum condition in various criteria, the multi-objective genetic algorithm is applied. Findings of performed experiments on datasets with various features demonstrate a resounding success in the proposed method.

목차

Abstract
 1. Introduction
 2. Spectral Clustering
  2.1. Ng-Jordan-Weiss (NJW) Method and its Improvement
  2.2. Previous Works on Selecting Eigenvectors
  2.3. SCWES Method
  2.4. ESBER Method
 3. The Proposed Method
 4. Evaluation of the Proposed Method
 5. Conclusion
 References

키워드

저자

• Rasoul Taghipour [ Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran ]
• Ehsan Asgarian [ Department of Computer Engineering, Ferdowsi University of Mashhad, Iran. ]
• Niloofar Chehrenama [ Department of Computer Engineering, Payam-Noor University of Mashhad, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605