Medical Document Clustering Using Ontology-Based Term Similarity Measures

doi:10.4018/jdwm.2008010104

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Medical Document Clustering Using Ontology-Based Term Similarity Measures

Xiaodan Zhang, Liping Jing, Xiaohua Hu, Michael Ng, Jiali Xia Jiangxi, Xiaohua Zhou

Source Title: International Journal of Data Warehousing and Mining (IJDWM)4(1)

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MLA

Zhang, Xiaodan, et al. "Medical Document Clustering Using Ontology-Based Term Similarity Measures." IJDWM vol.4, no.1 2008: pp.62-73. http://doi.org/10.4018/jdwm.2008010104

APA

Zhang, X., Jing, L., Hu, X., Ng, M., Jiangxi, J. X., & Zhou, X. (2008). Medical Document Clustering Using Ontology-Based Term Similarity Measures. International Journal of Data Warehousing and Mining (IJDWM), 4(1), 62-73. http://doi.org/10.4018/jdwm.2008010104

Chicago

Zhang, Xiaodan, et al. "Medical Document Clustering Using Ontology-Based Term Similarity Measures," International Journal of Data Warehousing and Mining (IJDWM) 4, no.1: 62-73. http://doi.org/10.4018/jdwm.2008010104

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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 cooccurred concepts. Spherical k-means are used for evaluate document vector re-weighting 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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Medical Document Clustering Using Ontology-Based Term Similarity Measures

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