Advanced clustering technique for medical data using semantic information
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shin, K. | - |
dc.contributor.author | Han, S.-Y. | - |
dc.contributor.author | Gelbukh, A | - |
dc.date.accessioned | 2023-03-09T01:15:40Z | - |
dc.date.available | 2023-03-09T01:15:40Z | - |
dc.date.issued | 2004-04 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.issn | 1611-3349 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65580 | - |
dc.description.abstract | MEDLINE is a representative collection of medical documents supplied with original full-text natural-language abstracts as well as with representative keywords (called MeSH-terms) manually selected by the expert annotators from a pre-defined ontology and structured according to their relation to the document. We show how the structured manually assigned semantic descriptions can be combined with the original full-text abstracts to improve quality of clustering the documents into a small number of clusters. As a baseline, we compare our results with clustering using only abstracts or only MeSH-terms. Our experiments show 36% to 47% higher cluster coherence, as well as more refined keywords for the produced clusters. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.title | Advanced clustering technique for medical data using semantic information | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/978-3-540-24694-7_33 | - |
dc.identifier.bibliographicCitation | MICAI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, v.2972, pp 322 - 331 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000221506600033 | - |
dc.identifier.scopusid | 2-s2.0-9444235094 | - |
dc.citation.endPage | 331 | - |
dc.citation.startPage | 322 | - |
dc.citation.title | MICAI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE | - |
dc.citation.volume | 2972 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.publisher.location | 독일 | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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