An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Semantic Clustering Using Multiple Ontologies
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Semantically-grounded construction of centroids for datasets with textual attributes
Knowledge-Based Systems
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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.