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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 (Drexel University, USA), Xiaohua Hu (Drexel University, USA and Jiangxi University of Finance and Economics, China), Jiali Xia (Jiangxi University of Finance and Economics, China), Xiaohua Zhou (Drexel University, USA), and Palakorn Achananuparp (Drexel University, USA)
Copyright: © 2008 |Pages: 18
DOI: 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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