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Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).<\/p>","DOI":"10.4018\/jdwm.2009080703","type":"journal-article","created":{"date-parts":[[2010,4,30]],"date-time":"2010-04-30T13:41:41Z","timestamp":1272634901000},"page":"44-57","source":"Crossref","is-referenced-by-count":6,"title":["A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature"],"prefix":"10.4018","volume":"5","author":[{"given":"Min","family":"Song","sequence":"first","affiliation":[{"name":"New Jersey Institute of Technology, USA"}]},{"given":"Xiaohua","family":"Hu","sequence":"additional","affiliation":[{"name":"Drexel University, USA"}]},{"given":"Illhoi","family":"Yoo","sequence":"additional","affiliation":[{"name":"University of Missouri, USA"}]},{"given":"Eric","family":"Koppel","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, USA"}]}],"member":"2432","reference":[{"key":"jdwm.2009080703-0","doi-asserted-by":"crossref","unstructured":"Aggarwal, C. 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