Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity.
BACKGROUND
Clustering MEDLINE documents is usually conducted by the vector space model, which computes the content similarity between two documents by basically using the inner-product of their word vectors. Recently, the semantic information of MeSH (Medical Subject Headings) thesaurus is being applied to clustering MEDLINE documents by mapping documents into MeSH concept vectors to be clustered. However, current approaches of using MeSH thesaurus have two serious limitations: first, important semantic information may be lost when generating MeSH concept vectors, and second, the content information of the original text has been discarded.


METHODS
Our new strategy includes three key points. First, we develop a sound method for measuring the semantic similarity between two documents over the MeSH thesaurus. Second, we combine both the semantic and content similarities to generate the integrated similarity matrix between documents. Third, we apply a spectral approach to clustering documents over the integrated similarity matrix.


RESULTS
Using various 100 datasets of MEDLINE records, we conduct extensive experiments with changing alternative measures and parameters. Experimental results show that integrating the semantic and content similarities outperforms the case of using only one of the two similarities, being statistically significant. We further find the best parameter setting that is consistent over all experimental conditions conducted. We finally show a typical example of resultant clusters, confirming the effectiveness of our strategy in improving MEDLINE document clustering.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Zhu
Shanfeng
S
Shanghai Key Lab of Intelligent Information Processing, Fudan University, School of Computer Science, Fudan University, Shanghai 200433, China. zhushanfeng@gmail.com
Zeng
Jia
J
Mamitsuka
Hiroshi
H
D000043:Abstracting and Indexing; D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D016247:Information Storage and Retrieval; D016239:MEDLINE; D046650:Medical Subject Headings; D010363:Pattern Recognition, Automated / Q000379:methods; D018875:Vocabulary, Controlled