K-maximin clustering: a maximin correlation approach to partition-based clustering
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Taehoon | - |
dc.contributor.author | Kim, Seung Jean | - |
dc.contributor.author | Chung, Eui-Young | - |
dc.contributor.author | Yoon, Sungroh | - |
dc.date.accessioned | 2021-09-08T13:39:25Z | - |
dc.date.available | 2021-09-08T13:39:25Z | - |
dc.date.created | 2021-06-11 | - |
dc.date.issued | 2009-09-10 | - |
dc.identifier.issn | 1349-2543 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/119322 | - |
dc.description.abstract | We propose a new clustering algorithm based upon the maximin correlation analysis (MCA), a learning technique that can minimize the maximum misclassification risk. The proposed algorithm resembles conventional partition clustering algorithms such as k-means in that data objects are partitioned into k disjoint partitions. On the other hand, the proposed approach is unique in that an MCA-based approach is used to decide the location of the representative point for each partition. We test the proposed technique with typography data and show our approach outperforms the popular k-means and k-medoids clustering in terms of retrieving the inherent cluster membership. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG | - |
dc.title | K-maximin clustering: a maximin correlation approach to partition-based clustering | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Sungroh | - |
dc.identifier.doi | 10.1587/elex.6.1205 | - |
dc.identifier.scopusid | 2-s2.0-70349471023 | - |
dc.identifier.wosid | 000271537400001 | - |
dc.identifier.bibliographicCitation | IEICE ELECTRONICS EXPRESS, v.6, no.17, pp.1205 - 1211 | - |
dc.relation.isPartOf | IEICE ELECTRONICS EXPRESS | - |
dc.citation.title | IEICE ELECTRONICS EXPRESS | - |
dc.citation.volume | 6 | - |
dc.citation.number | 17 | - |
dc.citation.startPage | 1205 | - |
dc.citation.endPage | 1211 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordAuthor | data mining | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | maximin correlation | - |
dc.subject.keywordAuthor | k-means | - |
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