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Medical Document Clustering Using Ontology-Based Term Similarity Measures
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Author(s): Xiaodan Zhang (Drexel University, USA), Liping Jing (The University of Hong Kong, China), Xiaohua Hu (Drexel University, USA), Michael Ng (Hong Kong Baptist University, China), Jiali Xia (Jiangxi University of Finance and Economics, China)and Xiaohua Zhou (Drexel University, USA)
Copyright: 2009
Pages: 12
Source title:
Medical Informatics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Joseph Tan (McMaster University, Canada)
DOI: 10.4018/978-1-60566-050-9.ch169
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Abstract
Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this article, we conduct a comparative study on how different term semantic similarity measures including path-based, information-content- based and feature-based similarity measure affect document clustering. Term re-weighting of document vector is an important method to integrate domain ontology to clustering process. In detail, the weight of a term is augmented by the weights of its co-occurred concepts. Spherical k-means are used for evaluate document vector reweighting on two real-world datasets: Disease10 and OHSUMED23. Experimental results on nine different semantic measures have shown that: (1) there is no certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms.
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