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.