Title: Biomedical ontology improves biomedical literature clustering performance: a comparison study

Authors: Illhoi Yoo, Xiaohua Hu, Il-Yeol Song

Addresses: 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

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

International Journal of Bioinformatics Research and Applications, 2007 Vol.3 No.3, pp.414 - 428

Published online: 04 Sep 2007 *

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