원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.4
2016.04
pp.417-426
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보