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A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

Min Song, Xiaohua Hu, Illhoi Yoo, Eric Koppel
Copyright: © 2009 |Volume: 5 |Issue: 4 |Pages: 14
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781616920425|EISSN: 1548-3924|DOI: 10.4018/jdwm.2009080703
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MLA

Song, Min, et al. "A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature." IJDWM vol.5, no.4 2009: pp.44-57. http://doi.org/10.4018/jdwm.2009080703

APA

Song, M., Hu, X., Yoo, I., & Koppel, E. (2009). A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature. International Journal of Data Warehousing and Mining (IJDWM), 5(4), 44-57. http://doi.org/10.4018/jdwm.2009080703

Chicago

Song, Min, et al. "A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature," International Journal of Data Warehousing and Mining (IJDWM) 5, no.4: 44-57. http://doi.org/10.4018/jdwm.2009080703

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Abstract

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this article, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).

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