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2006
IEEE

Biomedical Ontology MeSH Improves Document Clustering Qualify on MEDLINE Articles: A Comparison Study

9 years 5 months ago
Biomedical Ontology MeSH Improves Document Clustering Qualify on MEDLINE Articles: A Comparison Study
Document clustering has been used for better document retrieval, document browsing, and text mining. In this paper, we investigate if biomedical ontology MeSH improves the clustering quality for MEDLINE articles. For this investigation, we perform a comprehensive comparison study of various document clustering approaches such as hierarchical clustering methods (single-link, complete-link, and complete link), Bisecting K-means, K-means, and Suffix Tree Clustering (STC) in terms of efficiency, effectiveness, and scalability. According to our experiment results, biomedical ontology MeSH 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 MeSH ontology while hierarchical algorithms showing the poorest clustering quality do not reap the benefit of MeSH ontology.
Illhoi Yoo, Xiaohua Hu
Added 10 Jun 2010
Updated 10 Jun 2010
Type Conference
Year 2006
Where CBMS
Authors Illhoi Yoo, Xiaohua Hu
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