Biomedical ontology improves biomedical literature clustering performance: a comparison study.
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.
Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, Columbia, MO 65211, USA. yooil@health.missouri.edu
Illhoi
I
Yoo
Xiaohua
X
Hu
Il-Yeol
IY
Song
D016000:Cluster Analysis; D019295:Computational Biology; D016247:Information Storage and Retrieval; D016239:MEDLINE; D046650:Medical Subject Headings; D011642:Publications