Document clustering has been used for better document retrieval, document browsing, and text mining. In this paper, we investigate if biomedical ontology MeSH improves the clustering quality for MEDLINE articles. For this investigation, we perform a comprehensive comparison study of various document clustering approaches such as hierarchical clustering methods (single-link, complete-link, and complete link), Bisecting K-means, K-means, and Suffix Tree Clustering (STC) in terms of efficiency, effectiveness, and scalability. According to our experiment results, biomedical ontology MeSH significantly enhances clustering quality on biomedical documents. In addition, our results show that decent document clustering approaches, such as Bisecting Kmeans, K-means and STC, gains some benefit from MeSH ontology while hierarchical algorithms showing the poorest clustering quality do not reap the benefit of MeSH ontology.
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