Abstract
Clustering was usually applied on numerical and categorical information. However, textual information is acquiring an increasing importance with the appearance of methods for textual data mining. This paper proposes the use of classical clustering algorithms with a mixed function that combines numerical, categorical and semantic features. The content of the semantic features is extracted from textual data. This paper analyses and compares the behavior of different existing semantic similarity functions that use WordNet as background ontology. The different partitions obtained with the clustering algorithm are compared to human classifications in order to see which one approximates better the human reasoning. Moreover, the interpretability of the obtained clusters is discussed. The results show that those similarity measures that provide better results when compared using a standard benchmark also provide better and more interpretable partitions.
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Batet, M., Valls, A., Gibert, K. (2010). Performance of Ontology-Based Semantic Similarities in Clustering. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_36
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DOI: https://doi.org/10.1007/978-3-642-13208-7_36
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