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A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information

A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information

Xiaodan Zhang, Xiaohua Hu, Jiali Xia, Xiaohua Zhou, Palakorn Achananuparp
Copyright: © 2008 |Volume: 4 |Issue: 4 |Pages: 18
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202027|EISSN: 1548-3924|DOI: 10.4018/jdwm.2008100105
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MLA

Zhang, Xiaodan, et al. "A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information." IJDWM vol.4, no.4 2008: pp.84-101. http://doi.org/10.4018/jdwm.2008100105

APA

Zhang, X., Hu, X., Xia, J., Zhou, X., & Achananuparp, P. (2008). A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information. International Journal of Data Warehousing and Mining (IJDWM), 4(4), 84-101. http://doi.org/10.4018/jdwm.2008100105

Chicago

Zhang, Xiaodan, et al. "A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information," International Journal of Data Warehousing and Mining (IJDWM) 4, no.4: 84-101. http://doi.org/10.4018/jdwm.2008100105

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Abstract

In this article, we present a graph-based knowledge representation for biomedical digital library literature clustering. An efficient clustering method is developed to identify the ontology-enriched k-highest density term subgraphs that capture the core semantic relationship information about each document cluster. The distance between each document and the k term graph clusters is calculated. A document is then assigned to the closest term cluster. The extensive experimental results on two PubMed document sets (Disease10 and OHSUMED23) show that our approach is comparable to spherical k-means. The contributions of our approach are the following: (1) we provide two corpus-level graph representations to improve document clustering, a term co-occurrence graph and an abstract-title graph; (2) we develop an efficient and effective document clustering algorithm by identifying k distinguishable class-specific core term subgraphs using terms’ global and local importance information; and (3) the identified term clusters give a meaningful explanation for the document clustering results.

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