한국정보과학회:학술대회논문집 (Proceedings of the Korean Information Science Society Conference)
- 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (1)
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- Pages.697-699
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- 2004
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- 1598-5164(pISSN)
Semidefinite Programming을 통한 그래프의 동시 분할법
K-Way Graph Partitioning: A Semidefinite Programming Approach
- Jaehwan, Kim (Department of Computer Science, POSTECH) ;
- Seungjin, Choi (Department of Computer Science, POSTECH) ;
- Sung-Yang, Bang (Department of Computer Science, POSTECH)
- 발행 : 2004.10.01
초록
Despite many successful spectral clustering algorithm (based on the spectral decomposition of Laplacian(1) or stochastic matrix(2) ) there are several unsolved problems. Most spectral clustering Problems are based on the normalized of algorithm(3) . are close to the classical graph paritioning problem which is NP-hard problem. To get good solution in polynomial time. it needs to establish its convex form by using relaxation. In this paper, we apply a novel optimization technique. semidefinite programming(SDP). to the unsupervised clustering Problem. and present a new multiple Partitioning method. Experimental results confirm that the Proposed method improves the clustering performance. especially in the Problem of being mixed with non-compact clusters compared to the previous multiple spectral clustering methods.
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