Earticle

초록

영어

Spectral clustering is a method of subspace clustering which is suitable for the data of any shape and converges to global optimal solution. By combining concepts of shared nearest neighbors and geodesic distance with spectral clustering, a self-adaptive spectral clustering based on geodesic distance and shared nearest neighbors was proposed. Experiments show that the improved spectral clustering algorithm can fully take into account the information of neighbors, but also measure the exact distance and better process the geodetic data.

목차

Abstract
 1. Introduction
 2. Shared Nearest Neighbors
  2.1. The concept of shared nearest neighbors
  2.2 Self-adaptive spectral clustering based on shared nearest neighbors(SSC-SNN)
 3. Geodesic Distance
  3.1. The concept of geodesic distance
  3.2 The calculation of geodesic distance
 4. Self-adaptive spectral clustering based on geodesic distance and shared nearest neighbors(SSC-GD&SNN)
 5. Experiment result
  5.1 Experiment environment
  5.2 Bi-moon data
  5.3 Circular data  
  5.4 Hat-shaped data
 6. Conclusions
 References

키워드

저자

• Chunmiao Yuan [ School of Computer Science &Software, Tianjin Polytechnic University, Tianjin, China ]
• Kaixiang Fan [ School of Computer Science &Software, Tianjin Polytechnic University, Tianjin, China ]
• Xuemei Sun [ School of Computer Science &Software, Tianjin Polytechnic University, Tianjin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605