2006 AComprehensiveComparStudyOfDocClustForBiomedDigiLibMEDLINE

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Abstract

Document clustering has been used for better document retrieval, document browsing, and text mining in digital library. In this paper, we perform a comprehensive comparison study of various document clustering approaches such as three hierarchical methods (single-link, complete-link, and complete link), Bisecting K-means, K-means, and Suffix Tree Clustering in terms of the efficiency, the effectiveness, and the scalability. In addition, we apply a domain ontology to document clustering to investigate if the ontology such as MeSH improves clustering quality for MEDLINE articles. Because an ontology is a formal, explicit specification of a shared conceptualization for a domain of interest, the use of ontologies is a natural way to solve traditional information retrieval problems such as synonym/hypernym/hyponym problems. We conducted fairly extensive experiments based on different evaluation metrics such as misclassification index, F-measure, cluster purity, and Entropy on very large article sets from MEDLINE, the largest biomedical digital library in biomedicine.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2006 AComprehensiveComparStudyOfDocClustForBiomedDigiLibMEDLINEXiaohua Hu
Illhoi Yoo		A Comprehensive Comparison Study of Document Clustering for a Biomedical Digital Library MEDLINEProceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Librarieshttp://www.mendeley.com/research/a-comprehensive-comparison-study-of-document-clustering-for-a-biomedical-digital-library-medline/2006
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