A k-way graph partitioning algorithm based on clustering by eigenvector
- Authors
- Choe, TY; Park, CI
- Issue Date
- 2004
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- COMPUTATIONAL SCIENCE - ICCS 2004, PT 2, PROCEEDINGS, v.3037, pp.598 - 601
- Journal Title
- COMPUTATIONAL SCIENCE - ICCS 2004, PT 2, PROCEEDINGS
- Volume
- 3037
- Start Page
- 598
- End Page
- 601
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/3461
- ISSN
- 0302-9743
- Abstract
- The recursive spectral bisection for the k-way graph partition has been underestimated because it tries to balance the bipartition strictly. However, by loosening the balancing constraint, the spectral bisection can identify clusters efficiently. We propose a k-way graph partitioning algorithm based on clustering using recursive spectral bisection. After a graph is divided into a partition, the partition is adjusted in order to meet the balancing constraint. Experimental results show that the clustering based k-way partitioning generates partitions with 83.8 similar to 108.4% cutsets compared to the strict recursive spectral bisections or multi-level partitions.
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Collections - School of Electronic Engineering > 1. Journal Articles
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