Article Abstract

	Title:	Biomedical ontology improves biomedical literature clustering performance: a comparison study
	Author:	Illhoi Yoo, Xiaohua Hu, Il-Yeol Song
	Address:	Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, Columbia, MO 65211, USA. ' College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA. ' College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA
	Journal:	International Journal of Bioinformatics Research and Applications 2007 - Vol. 3, No.3  pp. 414 - 428
	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.
	Keywords:	document clustering; biomedical literature; MEDLINE; biomedical ontology; MeSH; comparison study; bioinformatics; information retrieval; document retrieval; text mining.
	DOI:	10.1504/IJBRA.2007.015010
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