Abstract
In different applications (i.e., information retrieval, filtering or analysis), it is useful to detect similar terms and to provide the possibility to use them jointly. Clustering of terms is one of the methods which can be exploited for this. In our study, we propose to test three methods dedicated to the clustering of terms (hierarchical ascendant classification, Radius and maximum), to combine them with the semantic distance algorithms and to compare them through the results they provide when applied to terms from the pharmacovigilance area. The comparison indicates that the non disjoint clustering (Radius and maximum) outperform the disjoint clusters by 10 to up to 20 points in all the experiments.
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Dupuch, M., Engström, C., Silvestrov, S., Hamon, T., Grabar, N. (2013). Comparison of Clustering Approaches through Their Application to Pharmacovigilance Terms. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_9
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DOI: https://doi.org/10.1007/978-3-642-38326-7_9
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