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