A k-way graph partitioning algorithm based on clustering by eigenvector
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choe, TY | - |
dc.contributor.author | Park, CI | - |
dc.date.available | 2020-04-24T14:26:17Z | - |
dc.date.created | 2020-03-31 | - |
dc.date.issued | 2004 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/3461 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.title | A k-way graph partitioning algorithm based on clustering by eigenvector | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choe, TY | - |
dc.identifier.wosid | 000222045500081 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL SCIENCE - ICCS 2004, PT 2, PROCEEDINGS, v.3037, pp.598 - 601 | - |
dc.citation.title | COMPUTATIONAL SCIENCE - ICCS 2004, PT 2, PROCEEDINGS | - |
dc.citation.volume | 3037 | - |
dc.citation.startPage | 598 | - |
dc.citation.endPage | 601 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
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