Consensus optimizing both distance sum and radius
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Amir, Amihood | - |
dc.contributor.author | Landau, Gad M. | - |
dc.contributor.author | Na, Joong Chae | - |
dc.contributor.author | Park, Heejin | - |
dc.contributor.author | Park, Kunsoo | - |
dc.contributor.author | Sim, Jeong Seop | - |
dc.date.accessioned | 2022-12-20T21:20:55Z | - |
dc.date.available | 2022-12-20T21:20:55Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2009-08 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/176378 | - |
dc.description.abstract | The consensus string problem is finding a representative string (consensus) of a given set of strings. In this paper we deal with the consensus string problems optimizing both distance sum and radius, where the distance sum is the sum of (Hamming) distances from the strings in to the consensus and the radius is the longest (Hamming) distance from the strings in to the consensus. Although there have been results considering either distance sum or radius, there have been no results considering both as far as we know. We present two algorithms to solve the consensus string problems optimizing both distance sum and radius for three strings. The first algorithm finds the optimal consensus string that minimizes both distance sum and radius, and the second algorithm finds the bounded consensus string such that, given constants s and r, the distance sum is at most s and the radius is at most r. Both algorithms take linear time. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.title | Consensus optimizing both distance sum and radius | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Heejin | - |
dc.identifier.doi | 10.1007/978-3-642-03784-9_23 | - |
dc.identifier.scopusid | 2-s2.0-70350637476 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.5721 LNCS, pp.234 - 242 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 5721 LNCS | - |
dc.citation.startPage | 234 | - |
dc.citation.endPage | 242 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Linear time | - |
dc.subject.keywordPlus | Algorithms | - |
dc.subject.keywordPlus | Information retrieval | - |
dc.subject.keywordPlus | Information services | - |
dc.subject.keywordPlus | Optimization | - |
dc.subject.keywordPlus | Towers | - |
dc.subject.keywordPlus | Hamming distance | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-642-03784-9_23 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.