DC Field | Value | Language |
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dc.contributor.author | Cho, C | - |
dc.contributor.author | Kim, S | - |
dc.contributor.author | Lee, J | - |
dc.contributor.author | Lee, DW | - |
dc.date.accessioned | 2016-04-01T02:04:40Z | - |
dc.date.available | 2016-04-01T02:04:40Z | - |
dc.date.created | 2009-03-20 | - |
dc.date.issued | 2006-02-01 | - |
dc.identifier.issn | 0377-2217 | - |
dc.identifier.other | 2005-OAK-0000005446 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/24363 | - |
dc.description.abstract | Clustering multimodal datasets can be problematic when a conventional algorithm such as k-means is applied due to its implicit assumption of Gaussian distribution of the dataset. This paper proposes a tandem clustering process for multimodal data sets. The proposed method first divides the multimodal dataset into many small pre-clusters by applying k-means or fuzzy k-means algorithm. These pre-clusters are then clustered again by agglomerative hierarchical clustering method using Kullback-Leibler divergence as an initial measure of dissimilarity. Benchmark results show that the proposed approach is not only effective at extracting the multimodal clusters but also efficient in computational time and relatively robust at the presence of outliers. (c) 2004 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | - |
dc.subject | multivariate statistics | - |
dc.subject | artificial intelligence | - |
dc.subject | clustering | - |
dc.subject | multimodal dataset | - |
dc.subject | k-means algorithm | - |
dc.subject | EFFICIENT ALGORITHM | - |
dc.title | A tandem clustering process for multimodal datasets | - |
dc.type | Article | - |
dc.contributor.college | 산업경영공학과 | - |
dc.identifier.doi | 10.1016/j.ejor.2004.05.020 | - |
dc.author.google | Cho, C | - |
dc.author.google | Kim, S | - |
dc.author.google | Lee, J | - |
dc.author.google | Lee, DW | - |
dc.relation.volume | 168 | - |
dc.relation.issue | 3 | - |
dc.relation.startpage | 998 | - |
dc.relation.lastpage | 1008 | - |
dc.contributor.id | 10081901 | - |
dc.relation.journal | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, v.168, no.3, pp.998 - 1008 | - |
dc.identifier.wosid | 000232383200022 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 1008 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 998 | - |
dc.citation.title | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | - |
dc.citation.volume | 168 | - |
dc.contributor.affiliatedAuthor | Kim, S | - |
dc.contributor.affiliatedAuthor | Lee, J | - |
dc.identifier.scopusid | 2-s2.0-25144472808 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 3 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | multivariate statistics | - |
dc.subject.keywordAuthor | artificial intelligence | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | multimodal dataset | - |
dc.subject.keywordAuthor | k-means algorithm | - |
dc.relation.journalWebOfScienceCategory | Management | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Business & Economics | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
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