Journal article
Biomedical ontology improves biomedical literature clustering performance: a comparison study
International journal of bioinformatics research and applications, v 3(3), pp 414-428
2007
PMID: 18048199
Abstract
Document clustering has been used for better document retrieval and text mining. In this paper, we investigate if a biomedical ontology improves biomedical literature clustering performance in terms of the effectiveness and the scalability. For this investigation, we perform a comprehensive comparison study of various document clustering approaches such as hierarchical clustering methods, Bisecting K-means, K-means and Suffix Tree Clustering (STC). According to our experiment results, a biomedical ontology significantly enhances clustering quality on biomedical documents. In addition, our results show that decent document clustering approaches, such as Bisecting K-means, K-means and STC, gains some benefit from the ontology while hierarchical algorithms showing the poorest clustering quality do not reap the benefit of the biomedical ontology.
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9 citations in Scopus
Details
- Title
- Biomedical ontology improves biomedical literature clustering performance: a comparison study
- Creators
- Illhoi Yoo - Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, Columbia, MO 65211, USA. yooil@health.missouri.eduXiaohua HuIl-Yeol Song
- Publication Details
- International journal of bioinformatics research and applications, v 3(3), pp 414-428
- Publisher
- Switzerland
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-35348964654
- Other Identifier
- 991014878235004721