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 comparis...

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Bibliographic Details
Published inInternational journal of bioinformatics research and applications Vol. 3; no. 3; p. 414
Main Authors Yoo, Illhoi, Hu, Xiaohua, Song, Il-Yeol
Format Journal Article
LanguageEnglish
Published Switzerland 2007
Subjects
Cluster Analysis
Computational Biology
Information Storage and Retrieval
Medical Subject Headings
MEDLINE
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Summary: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.
ISSN:1744-5485
DOI:10.1504/IJBRA.2007.015010