Advanced clustering technique for medical data using semantic information
- Authors
- Shin, K.; Han, S.-Y.; Gelbukh, A
- Issue Date
- Apr-2004
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- MICAI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, v.2972, pp 322 - 331
- Pages
- 10
- Journal Title
- MICAI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE
- Volume
- 2972
- Start Page
- 322
- End Page
- 331
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65580
- DOI
- 10.1007/978-3-540-24694-7_33
- ISSN
- 0302-9743
1611-3349
- 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.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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